Signal processing for measurement of physiological analytes

ABSTRACT

A method is provided for continually or continuously measuring the concentration of target chemical analytes present in a biological system, and processing analyte-specific signals to obtain a measurement value that is closely correlated with the concentration of the target chemical analyte in the biological system. One important application of the invention involves a method for signal processing in a system for monitoring blood glucose values.

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application is related to provisional patent applicationserial No. 60/085,344, filed May 13, 1998, from which priority isclaimed under 35 USC §119(e)(1) and which application is incorporatedherein by reference in its entirety.

FIELD OF THE INVENTION

[0002] The invention relates generally to methods for continually orcontinuously measuring the concentration of target chemical analytespresent in a biological system. More particularly, the invention relatesto methods for processing signals obtained during measurement ofphysiological analytes. One important application of the inventioninvolves a method for monitoring blood glucose concentrations.

BACKGROUND OF THE INVENTION

[0003] A number of diagnostic tests are routinely performed on humans toevaluate the amount or existence of substances present in blood or otherbody fluids. These diagnostic tests typically rely on physiologicalfluid samples removed from a subject, either using a syringe or bypricking the skin. One particular diagnostic test entailsself-monitoring of blood glucose levels by diabetics.

[0004] Diabetes is a major health concern, and treatment of the moresevere form of the condition, Type I (insulin-dependent) diabetes,requires one or more insulin injections per day. Insulin controlsutilization of glucose or sugar in the blood and prevents hyperglycemiawhich, if left uncorrected, can lead to ketosis. On the other hand,improper administration of insulin therapy can result in hypoglycemicepisodes, which can cause coma and death. Hyperglycemia in diabetics hasbeen correlated with several long-term effects of diabetes, such asheart disease, atherosclerosis, blindness, stroke, hypertension andkidney failure.

[0005] The value of frequent monitoring of blood glucose as a means toavoid or at least minimize the complications of Type I diabetes is wellestablished. Patients with Type II (non-insulin-dependent) diabetes canalso benefit from blood glucose monitoring in the control of theircondition by way of diet and exercise.

[0006] Conventional blood glucose monitoring methods generally requirethe drawing of a blood sample (e.g., by fingerprick) for each test, anda determination of the glucose level using an instrument that readsglucose concentrations by electrochemical or calorimetric methods. TypeI diabetics must obtain several fingerprick blood glucose measurementseach day in order to maintain tight glycemic control. However, the painand inconvenience associated with this blood sampling, along with thefear of hypoglycemia, has led to poor patient compliance, despite strongevidence that tight control dramatically reduces long-term diabeticcomplications. In fact, these considerations can often lead to anabatement of the monitoring process by the diabetic. See, e.g., TheDiabetes Control and Complications Trial Research Group (1993) New Engl.J. Med. 329:977-1036.

[0007] Recently, various methods for determining the concentration ofblood analytes without drawing blood have been developed. For example,U.S. Pat. No. 5,267,152 to Yang et al. describes a noninvasive techniqueof measuring blood glucose concentration using near-IR radiationdiffuse-reflection laser spectroscopy. Similar near-IR spectrometricdevices are also described in U.S. Pat. No. 5,086,229 to Rosenthal etal. and U.S. Pat. No. 4,975,581 to Robinson et al.

[0008] U.S. Pat. No. 5,139,023 to Stanley et al., and U.S. Pat. No.5,443,080 to D'Angelo et al. describe transdermal blood glucosemonitoring devices that rely on a permeability enhancer (e.g., a bilesalt) to facilitate transdermal movement of glucose along aconcentration gradient established between interstitial fluid and areceiving medium. U.S. Pat. No. 5,036,861 to Sembrowich describes apassive glucose monitor that collects perspiration through a skin patch,where a cholinergic agent is used to stimulate perspiration secretionfrom the eccrine sweat gland. Similar perspiration collection devicesare described in U.S. Pat. No. 5,076,273 to Schoendorfer and U.S. Pat.No. 5,140,985 to Schroeder.

[0009] In addition, U.S. Pat. No. 5,279,543 to Glikfeld et al. describesthe use of iontophoresis to noninvasively sample a substance throughskin into a receptacle on the skin surface. Glikfeld teaches that thissampling procedure can be coupled with a glucose-specific biosensor orglucose-specific electrodes in order to monitor blood glucose. Finally,International Publication No. WO 96/00110, published Jan. 4, 1996,describes an iontophoretic apparatus for transdermal monitoring of atarget substance, wherein an iontophoretic electrode is used to move ananalyte into a collection reservoir and a biosensor is used to detectthe target analyte present in the reservoir.

SUMMARY OF THE INVENTION

[0010] The present invention provides a method for continually orcontinuously measuring the concentration of an analyte present in abiological system. The method entails continually or continuouslydetecting an analyte from the biological system and deriving a rawsignal therefrom, wherein the raw signal is related to the analyteconcentration. A number of signal processing steps are then carried outin order to convert the raw signal into an initial signal output that isindicative of an analyte amount. The converted signal is then furtherconverted into a value indicative of the concentration of analytepresent in the biological system.

[0011] The raw signal can be obtained using any suitable sensingmethodology including, for example, methods which rely on direct contactof a sensing apparatus with the biological system; methods which extractsamples from the biological system by invasive, minimally invasive, andnon-invasive sampling techniques, wherein the sensing apparatus iscontacted with the extracted sample; methods which rely on indirectcontact of a sensing apparatus with the biological system; and the like.In preferred embodiments of the invention, methods are used to extractsamples from the biological sample using minimally invasive ornon-invasive sampling techniques. The sensing apparatus used with any ofthe above-noted methods can employ any suitable sensing element toprovide the raw signal including, but not limited to, physical,chemical, electrochemical, photochemical, spectrophotometric,polarimetric, calorimetric, radiometric, or like elements. In preferredembodiments of the invention, a biosensor is used which comprises anelectrochemical sensing element.

[0012] In one particular embodiment of the invention, the raw signal isobtained using a transdermal sampling system that is placed in operativecontact with a skin or mucosal surface of the biological system. Thesampling system transdermally extracts the analyte from the biologicalsystem using any appropriate sampling technique, for example,iontophoresis. The transdermal sampling system is maintained inoperative contact with the skin or mucosal surface of the biologicalsystem to provide for such continual or continuous analyte measurement.

[0013] The analyte can be any specific substance or component that oneis desirous of detecting and/or measuring in a chemical, physical,enzymatic, or optical analysis. Such analytes include, but are notlimited to, amino acids, enzyme substrates or products indicating adisease state or condition, other markers of disease states orconditions, drugs of abuse, therapeutic and/or pharmacologic agents,electrolytes, physiological analytes of interest (e.g., calcium,potassium, sodium, chloride, bicarbonate (CO₂), glucose, urea (bloodurea nitrogen), lactate, hematocrit, and hemoglobin), lipids, and thelike. In preferred embodiments, the analyte is a physiological analyteof interest, for example glucose, or a chemical that has a physiologicalaction, for example a drug or pharmacological agent.

[0014] Accordingly, it is an object of the invention to provide a methodfor continually or continuously measuring an analyte present in abiological system, wherein raw signals are obtained from a suitablesensing apparatus, and then subjected to signal processing techniques.More particularly, the raw signals undergo a data screening method inorder to eliminate outlier signals and/or poor (incorrect) signals usinga predefined set of selection criteria. In addition, or alternatively,the raw signal can be converted in a conversion step which (i) removesor corrects for background information, (ii) integrates the raw signalover a sensing time period, (iii) performs any process which convertsthe raw signal from one signal type to another, or (iv) performs anycombination of steps (i), (ii) and/or (iii). In preferred embodiments,the conversion step entails a baseline background subtraction method toremove background from the raw signal and an integration step. In otherembodiments, the conversion step can be tailored for use with a sensingdevice that provides both active and reference (blank) signals; whereinmathematical transformations are used to individually smooth active andreference signals, and/or to subtract a weighted reference (blank)signal from the active signal. In still further embodiments, theconversion step includes correction functions which account for changingconditions in the biological system and/or the biosensor system (e.g.,temperature fluctuations in the biological system, temperaturefluctuations in the sensor element, skin conductivity fluctuations, orcombinations thereof). The result of the conversion step is an initialsignal output which provides a value which can be correlated with theconcentration of the target analyte in the biological sample.

[0015] It is also an object of the invention to provide a signalprocessing calibration step, wherein the raw or initial signals obtainedas described above are converted into an analyte-specific value of knownunits to provide an interpretation of the signal obtained from thesensing device. The interpretation uses a mathematical transformation tomodel the relationship between a measured response in the sensing deviceand a corresponding analyte-specific value. Such mathematicaltransformations can entail the use of linear or nonlinear regressions,or neural network algorithms. In one embodiment, the calibration stepentails calibrating the sensing device using a single- or multi-pointcalibration, and then converting post-calibration data using correlationfactors, time corrections and constants to obtain an analyte-specificvalue. Further signal processing can be used to refine the informationobtained in the calibration step, for example, where a signal processingstep is used to correct for signal differences due to variableconditions unique to the sensor element used to obtain the raw signal.In one embodiment, this further step is used to correct for signaltime-dependence, particularly signal decline. In another embodiment, aconstant offset term is obtained, which offset is added to the signal toaccount for a non-zero signal at an estimated zero analyteconcentration.

[0016] Further, the methods of the present invention include enhancementof skin permeability by pricking the skin with micro-needles. Inaddition, the sampling system can be programed to begin execution ofsampling and sensing at a defined time(s).

[0017] It is yet a further object of the invention to provide amonitoring system for continually or continuously measuring an analytepresent in a biological system. The monitoring system comprises, inoperative combination: (a) a sampling means for continually orcontinuously extracting the analyte from the biological system, (b) asensing means in operative contact with the analyte extracted by thesampling means, and (c) a microprocessor means in operativecommunication with the sensing means. The sampling means is adapted forextracting the analyte across a skin or mucosal surface of a biologicalsystem. The sensing means is used to obtain a raw signal from theextracted analyte, wherein the raw signal is specifically related to theanalyte. The microprocessor means is used to subject the raw signal to aconversion step, thereby converting the same into an initial signaloutput which is indicative of the amount of analyte extracted by thesampling means, and then perform a calibration step which correlates theinitial signal output with a measurement value indicative of theconcentration of analyte present in the biological system at the time ofextraction. In one embodiment, the monitoring system uses iontophoresisto extract the analyte from the biological system. In other embodiments,the monitoring system is used to extract a glucose analyte from thebiological system. Further, the microprocessor can be programed to beginexecution of sampling and sensing at a defined time(s).

[0018] Additional objects, advantages and novel features of theinvention will be set forth in part in the description which follows,and in part will become apparent to those skilled in the art uponexamination of the following, or may be learned by practice of theinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019]FIG. 1A depicts a top plan view of an iontophoretic collectionreservoir and electrode assembly for use in a transdermal samplingdevice constructed according to the present invention.

[0020]FIG. 1B depicts the side view of the iontophoretic collectionreservoir and electrode assembly shown in FIG. 1A.

[0021]FIG. 2 is a pictorial representation of an iontophoretic samplingdevice which includes the iontophoretic collection reservoir andelectrode assembly of FIGS. 1A and 1B.

[0022]FIG. 3 is a representation of one embodiment of a bimodalelectrode design. The figure presents an overhead and schematic view ofthe electrode assembly 33. In the figure, the bimodal electrode is shownat 30 and can be, for example, a Ag/AgCl iontophoretic/counterelectrode. The sensing or working electrode (made from, for example,platinum) is shown at 31. The reference electrode is shown at 32 and canbe, for example, a Ag/AgCl electrode. The components are mounted on asuitable nonconductive substrate 34, for example, plastic or ceramic.The conductive leads 37 leading to the connection pad 35 are covered bya second nonconductive piece 36 of similar or different material. Inthis example of such an electrode the working electrode area isapproximately 1.35 cm². The dashed line in FIG. 3 represents the planeof the cross-sectional schematic view presented in FIG. 4.

[0023]FIG. 4 is a representation of a cross-sectional schematic view ofthe bimodal electrodes as they may be used in conjunction with areference electrode and a hydrogel pad. In the figure, the componentsare as follows: bimodal electrodes 40 and 41; sensing electrodes 42 and43; reference electrodes 44 and 45; a substrate 46; and hydrogel pads 47and 48.

[0024]FIG. 5 is an exploded pictorial representation of components froma preferred embodiment of the automatic sampling system of the presentinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] Before describing the present invention in detail, it is to beunderstood that this invention is not limited to particular compositionsor biological systems as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting.

[0026] It must be noted that, as used in this specification and theappended claims, the singular forms “a”, “an” and “the” include pluralreferents unless the content clearly dictates otherwise. Thus, forexample, reference to “a time-dependent variable” includes a mixture oftwo or more such variables, reference to “an electrochemically activespecies” includes two or more such species, reference to “an analyte”includes mixtures of analytes, and the like.

[0027] All publications, patents and patent applications cited herein,whether supra or infra, are hereby incorporated by reference in theirentirety.

[0028] Unless defined otherwise, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although any methodsand materials similar or equivalent to those described herein can beused in the practice for testing of the present invention, the preferredmaterials and methods are described herein.

[0029] In describing and claiming the present invention, the followingterminology will be used in accordance with the definitions set outbelow.

[0030] Definitions

[0031] The terms “analyte” and “target analyte” are used herein todenote any physiological analyte of interest that is a specificsubstance or component that is being detected and/or measured in achemical, physical, enzymatic, or optical analysis. A detectable signal(e.g., a chemical signal or electrochemical signal) can be obtained,either directly or indirectly, from such an analyte or derivativesthereof. Furthermore, the terms “analyte” and “substance” are usedinterchangeably herein, and are intended to have the same meaning, andthus encompass any substance of interest. In preferred embodiments, theanalyte is a physiological analyte of interest, for example, glucose, ora chemical that has a physiological action, for example, a drug orpharmacological agent.

[0032] A “sampling device” or “sampling system” refers to any device forobtaining a sample from a biological system for the purpose ofdetermining the concentration of an analyte of interest. As used herein,the term “sampling” means invasive, minimally invasive or non-invasiveextraction of a substance from the biological system, generally across amembrane such as skin or mucosa. The membrane can be natural orartificial, and can be of plant or animal nature, such as natural orartificial skin, blood vessel tissue, intestinal tissue, and the like.Typically, the sampling means are in operative contact with a“reservoir,” or “collection reservoir,” wherein the sampling means isused for extracting the analyte from the biological system into thereservoir to obtain the analyte in the reservoir. A “biological system”includes both living and artificially maintained systems. Examples ofminimally invasive and noninvasive sampling techniques includeiontophoresis, sonophoresis, suction, electroporation, thermal poration,passive diffusion, microfine (miniature) lances or cannulas,subcutaneous implants or insertions, and laser devices. Sonophoresisuses ultrasound to increase the permeability of the skin (see, e.g.,Menon et al. (1994) Skin Pharmacology 7:130-139). Suitable sonophoresissampling systems are described in International Publication No. WO91/12772, published Sep. 5, 1991. Passive diffusion sampling devices aredescribed, for example, in International Publication Nos.: WO 97/38126(published Oct. 16, 1997); WO 97/42888, WO 97/42886, WO 97/42885, and WO97/42882 (all published Nov. 20, 1997); and WO 97/43962 (published Nov.27, 1997). Laser devices use a small laser beam to burn a hole throughthe upper layer of the patient's skin (see, e.g., Jacques et al. (1978)J. Invest. Dermatology 88:88-93). Examples of invasive samplingtechniques include traditional needle and syringe or vacuum sample tubedevices.

[0033] The term “collection reservoir” is used to describe any suitablecontainment means for containing a sample extracted from a biologicalsystem. For example, the collection reservoir can be a receptaclecontaining a material which is ionically conductive (e.g., water withions therein), or alternatively, it can be a material, such as, asponge-like material or hydrophilic polymer, used to keep the water inplace. Such collection reservoirs can be in the form of a hydrogel (forexample, in the form of a disk or pad). Hydrogels are typically referredto as “collection inserts.” Other suitable collection reservoirsinclude, but are not limited to, tubes, vials, capillary collectiondevices, cannulas, and miniaturized etched, ablated or molded flowpaths.

[0034] A “housing” for the sampling system can further include suitableelectronics (e.g., microprocessor, memory, display and other circuitcomponents) and power sources for operating the sampling system in anautomatic fashion.

[0035] A “monitoring system,” as used herein, refers to a system usefulfor continually or continuously measuring a physiological analytepresent in a biological system. Such a system typically includes, but isnot limited to, sampling means, sensing means, and a microprocessormeans in operative communication with the sampling means and the sensingmeans.

[0036] The term “artificial,” as used herein, refers to an aggregationof cells of monolayer thickness or greater which are grown or culturedin vivo or in vitro, and which function as a tissue of an organism butare not actually derived, or excised, from a pre-existing source orhost.

[0037] The term “subject” encompasses any warm-blooded animal,particularly including a member of the class Mammalia such as, withoutlimitation, humans and nonhuman primates such as chimpanzees and otherapes and monkey species; farm animals such as cattle, sheep, pigs, goatsand horses; domestic mammals such as dogs and cats; laboratory animalsincluding rodents such as mice, rats and guinea pigs, and the like. Theterm does not denote a particular age or sex. Thus, adult and newbornsubjects, as well as fetuses, whether male or female, are intended to becovered.

[0038] As used herein, the term “continual measurement” intends a seriesof two or more measurements obtained from a particular biologicalsystem, which measurements are obtained using a single device maintainedin operative contact with the biological system over the time period inwhich the series of measurements is obtained. The term thus includescontinuous measurements.

[0039] The term “transdermal,” as used herein, includes both transdermaland transmucosal techniques, i.e., extraction of a target analyte acrossskin or mucosal tissue. Aspects of the invention which are describedherein in the context of “transdermal,” unless otherwise specified, aremeant to apply to both transdermal and transmucosal techniques.

[0040] The term “transdermal extraction,” or “transdermally extracted”intends any noninvasive, or at least minimally invasive sampling method,which entails extracting and/or transporting an analyte from beneath atissue surface across skin or mucosal tissue. The term thus includesextraction of an analyte using iontophoresis (reverse iontophoresis),electroosmosis, sonophoresis, microdialysis, suction, and passivediffusion. These methods can, of course, be coupled with application ofskin penetration enhancers or skin permeability enhancing technique suchas tape stripping or pricking with micro-needles. The term“transdermally extracted” also encompasses extraction techniques whichemploy thermal poration, electroporation, microfine lances, microfinecanulas, subcutaneous implants or insertions, and the like.

[0041] The term “iontophoresis” intends a method for transportingsubstances across tissue by way of an application of electrical energyto the tissue. In conventional iontophoresis, a reservoir is provided atthe tissue surface to serve as a container of material to betransported. Iontophoresis can be carried out using standard methodsknown to those of skill in the art, for example, by establishing anelectrical potential using a direct current (DC) between fixed anode andcathode “iontophoretic electrodes,” alternating a direct current betweenanode and cathode iontophoretic electrodes, or using a more complexwaveform such as applying a current with alternating polarity (AP)between iontophoretic electrodes (so that each electrode is alternatelyan anode or a cathode).

[0042] The term “reverse iontophoresis” refers to the movement of asubstance from a biological fluid across a membrane by way of an appliedelectric potential or current. In reverse iontophoresis, a reservoir isprovided at the tissue surface to receive the extracted material.

[0043] “Electroosmosis” refers to the movement of a substance through amembrane by way of an electric field-induced convective flow. The termsiontophoresis, reverse iontophoresis, and electroosmosis, will be usedinterchangeably herein to refer to movement of any ionically charged oruncharged substance across a membrane (e.g., an epithelial membrane)upon application of an electric potential to the membrane through anionically conductive medium.

[0044] The term “sensing device,” “sensing means,” or “biosensor device”encompasses any device that can be used to measure the concentration ofan analyte, or derivative thereof, of interest. Preferred sensingdevices for detecting blood analytes generally include electrochemicaldevices and chemical devices. Examples of electrochemical devicesinclude the Clark electrode system (see, e.g., Updike, et al., (1967)Nature 214:986-988), and other amperometric, coulometric, orpotentiometric electrochemical devices. Examples of chemical devicesinclude conventional enzyme-based reactions as used in the Lifescan®glucose monitor (Johnson and Johnson, New Brunswick, N.J.) (see, e.g.,U.S. Pat. No. 4,935,346 to Phillips, et al.).

[0045] A “biosensor” or “biosensor device” includes, but is not limitedto, a “sensor element” which includes, but is not limited to, a“biosensor electrode” or “sensing electrode” or “working electrode”which refers to the electrode that is monitored to determine the amountof electrical signal at a point in time or over a given time period,which signal is then correlated with the concentration of a chemicalcompound. The sensing electrode comprises a reactive surface whichconverts the analyte, or a derivative thereof, to electrical signal. Thereactive surface can be comprised of any electrically conductivematerial such as, but not limited to, platinum-group metals (including,platinum, palladium, rhodium, ruthenium, osmium, and iridium), nickel,copper, silver, and carbon, as well as, oxides, dioxides, combinationsor alloys thereof. Some catalytic materials, membranes, and fabricationtechnologies suitable for the construction of amperometric biosensorswere described by Newman, J. D., et al. (Analytical Chemistry 67(24),4594-4599, 1995).

[0046] The “sensor element” can include components in addition to abiosensor electrode, for example, it can include a “referenceelectrode,” and a “counter electrode.” The term “reference electrode” isused herein to mean an electrode that provides a reference potential,e.g., a potential can be established between a reference electrode and aworking electrode. The term “counter electrode” is used herein to meanan electrode in an electrochemical circuit which acts as a currentsource or sink to complete the electrochemical circuit. Although it isnot essential that a counter electrode be employed where a referenceelectrode is included in the circuit and the electrode is capable ofperforming the function of a counter electrode, it is preferred to haveseparate counter and reference electrodes because the referencepotential provided by the reference electrode is most stable when it isat equilibrium. If the reference electrode is required to act further asa counter electrode, the current flowing through the reference electrodemay disturb this equilibrium. Consequently, separate electrodesfunctioning as counter and reference electrodes are most preferred.

[0047] In one embodiment, the “counter electrode” of the “sensorelement” comprises a “bimodal electrode.” The term “bimodal electrode”as used herein typically refers to an electrode which is capable offunctioning non-simultaneously as, for example, both the counterelectrode (of the “sensor element”) and the iontophoretic electrode (ofthe “sampling means”).

[0048] The terms “reactive surface,” and “reactive face” are usedinterchangeably herein to mean the surface of the sensing electrodethat: (1) is in contact with the surface of an electrolyte containingmaterial (e.g. gel) which contains an analyte or through which ananalyte, or a derivative thereof, flows from a source thereof; (2) iscomprised of a catalytic material (e.g., carbon, platinum, palladium,rhodium, ruthenium, or nickel and/or oxides, dioxides and combinationsor alloys thereof) or a material that provides sites for electrochemicalreaction; (3) converts a chemical signal (e.g. hydrogen peroxide) intoan electrical signal (e.g., an electrical current); and (4) defines theelectrode surface area that, when composed of a reactive material, issufficient to drive the electrochemical reaction at a rate sufficient togenerate a detectable, reproducibly measurable, electrical signal thatis correlatable with the amount of analyte present in the electrolyte.

[0049] The term “collection reservoir” and “collection insert” are usedto describe any suitable containment means for containing a sampleextracted from a biological system. The reservoir can include a materialwhich is ionically conductive (e.g., water with ions therein), whereinanother material such as a sponge-like material or hydrophilic polymeris used to keep the water in place. Such collection reservoirs can be inthe form of a hydrogel (for example, in the shape of a disk or pad).Other suitable collection reservoirs include, but are not limited to,tubes, vials, capillary collection devices, cannulas, and miniaturizedetched, ablated or molded flow paths.

[0050] An “ionically conductive material” refers to any material thatprovides ionic conductivity, and through which electrochemically activespecies can diffuse. The ionically conductive material can be, forexample, a solid, liquid, or semi-solid (e.g., in the form of a gel)material that contains an electrolyte, which can be composed primarilyof water and ions (e.g., sodium chloride), and generally comprises 50%or more water by weight. The material can be in the form of a gel, asponge or pad (e.g., soaked with an electrolytic solution), or any othermaterial that can contain an electrolyte and allow passage therethroughof electrochemically active species, especially the analyte of interest.

[0051] The term “physiological effect” encompasses effects produced inthe subject that achieve the intended purpose of a therapy. In preferredembodiments, a physiological effect means that the symptoms of thesubject being treated are prevented or alleviated. For example, aphysiological effect would be one that results in the prolongation ofsurvival in a patient.

[0052] A “laminate”, as used herein, refers to structures comprised ofat least two bonded layers. The layers may be bonded by welding orthrough the use of adhesives. Examples of welding include, but are notlimited to, the following: ultrasonic welding, heat bonding, andinductively coupled localized heating followed by localized flow.Examples of common adhesives include, but are not limited to, pressuresensitive adhesives, thermoset adhesives, cyanocrylate adhesives,epoxies, contact adhesives, and heat sensitive adhesives.

[0053] A “collection assembly”, as used herein, refers to structurescomprised of several layers, where the assembly includes at least onecollection insert, for example a hydrogel. An example of a collectionassembly of the present invention is a mask layer, collection inserts,and a retaining layer where the layers are held in appropriate,functional relationship to each other but are not necessarily alaminate, i.e., the layers may not be bonded together. The layers may,for example, be held together by interlocking geometry or friction.

[0054] An “autosensor assembly”, as used herein, refers to structuresgenerally comprising a mask layer, collection inserts, a retaininglayer, an electrode assembly, and a support tray. The autosensorassembly may also include liners. The layers of the assembly are held inappropriate, functional relationship to each other.

[0055] The mask and retaining layers are preferably composed ofmaterials that are substantially impermeable to the analyte (chemicalsignal) to be detected (e.g., glucose); however, the material can bepermeable to other substances. By “substantially impermeable” is meantthat the material reduces or eliminates chemical signal transport (e.g.,by diffusion). The material can allow for a low level of chemical signaltransport, with the proviso that chemical signal that passes through thematerial does not cause significant edge effects at the sensingelectrode.

[0056] “Substantially planar” as used herein, includes a planar surfacethat contacts a slightly curved surface, for example, a forearm or upperarm of a subject. A “substantially planar” surface is, for example, asurface having a shape to which skin can conform, i.e., contactingcontact between the skin and the surface.

[0057] By the term “printed” as used herein is meant a substantiallyuniform deposition of an electrode formulation onto one surface of asubstrate (i.e., the base support). It will be appreciated by thoseskilled in the art that a variety of techniques may be used to effectsubstantially uniform deposition of a material onto a substrate, e.g.,Gravure-type printing, extrusion coating, screen coating, spraying,painting, or the like.

[0058] The term “enzyme” intends any compound or material whichcatalyzes a reaction between molecules to produce one or more reactionproducts. The term thus includes protein enzymes, or enzymaticallyactive portions (fragments) thereof, which proteins and/or proteinfragments may be isolated from a natural source, or recombinantly orsynthetically produced. The term also encompasses designed syntheticenzyme mimetics.

[0059] The term “time-dependent signal decline” refers to a detectabledecrease in measured signal over time when no decrease or change inanalyte concentration is actually occurring. The decrease in signal overtime may be due to a number of different phenomena.

[0060] The term “signal-to-noise ratio” describes the relationshipbetween the actual signal intended to be measured and the variation insignal in the absence of the analyte. The terms “S/N” and “SNR” are alsoused to refer to the signal-to-noise ratio. “Noise,” as used herein,refers to any undesirable signal which is measured along with theintended signal.

[0061] General Methods

[0062] The present invention relates to use of a device fortransdermally extracting and measuring the concentration of a targetanalyte present in a biological system. In preferred embodiments, thesensing device comprises a biosensor. In other preferred embodiments, asampling device is used to extract small amounts of a target analytefrom the biological system, and then sense and/or quantify theconcentration of the target analyte. Measurement with the biosensorand/or sampling with the sampling device can be carried out in acontinual or continuous manner. Continual or continuous measurementsallow for closer monitoring of target analyte concentrationfluctuations.

[0063] The analyte can be any specific substance or component that oneis desirous of detecting and/or measuring in a chemical, physical,enzymatic, or optical analysis. Such analytes include, but are notlimited to, amino acids, enzyme substrates or products indicating adisease state or condition, other markers of disease states orconditions, drugs of abuse, therapeutic and/or pharmacologic agents(e.g., theophylline, anti-HIV drugs, lithium, anti-epileptic drugs,cyclosporin, chemotherapeutics), electrolytes, physiological analytes ofinterest (e.g., urate/uric acid, carbonate, calcium, potassium, sodium,chloride, bicarbonate (CO₂), glucose, urea (blood urea nitrogen),lactate/lactic acid, hydroxybutyrate, cholesterol, triglycerides,creatine, creatinine, insulin, hematocrit, and hemoglobin), blood gases(carbon dioxide, oxygen, pH), lipids, heavy metals (e.g., lead, copper),and the like. In preferred embodiments, the analyte is a physiologicalanalyte of interest, for example glucose, or a chemical that has aphysiological action, for example a drug or pharmacological agent.

[0064] In order to facilitate detection of the analyte, an enzyme can bedisposed in the collection reservoir, or, if several collectionreservoirs are used, the enzyme can be disposed in several or all of thereservoirs. The selected enzyme is capable of catalyzing a reaction withthe extracted analyte (in this case glucose) to the extent that aproduct of this reaction can be sensed, e.g., can be detectedelectrochemically from the generation of a current which current isdetectable and proportional to the concentration or amount of theanalyte which is reacted. A suitable enzyme is glucose oxidase whichoxidizes glucose to gluconic acid and hydrogen peroxide. The subsequentdetection of hydrogen peroxide on an appropriate biosensor electrodegenerates two electrons per hydrogen peroxide molecule which create acurrent which can be detected and related to the amount of glucoseentering the device. Glucose oxidase (GOx) is readily availablecommercially and has well known catalytic characteristics. However,other enzymes can also be used, so long as they specifically catalyze areaction with an analyte or substance of interest to generate adetectable product in proportion to the amount of analyte so reacted.

[0065] In like manner, a number of other analyte-specific enzyme systemscan be used in the invention, which enzyme systems operate on much thesame general techniques. For example, a biosensor electrode that detectshydrogen peroxide can be used to detect ethanol using an alcohol oxidaseenzyme system, or similarly uric acid with urate oxidase system, ureawith a urease system, cholesterol with a cholesterol oxidase system, andtheophylline with a xanthine oxidase system.

[0066] In addition, the oxidase enzyme (used for hydrogen peroxide-baseddetection) can be replaced with another redox system, for example, thedehydrogenase-enzyme NAD-NADH, which offers a separate route todetecting additional analytes. Dehydrogenase-based sensors can useworking electrodes made of gold or carbon (via mediated chemistry).Examples of analytes suitable for this type of monitoring include, butare not limited to, cholesterol, ethanol, hydroxybutyrate,phenylalanine, triglycerides, and urea. Further, the enzyme can beeliminated and detection can rely on direct electrochemical orpotentiometric detection of an analyte. Such analytes include, withoutlimitation, heavy metals (e.g., cobalt, iron, lead, nickel, zinc),oxygen, carbonate/carbon dioxide, chloride, fluoride, lithium, pH,potassium, sodium, and urea. Also, the sampling system described hereincan be used for therapeutic drug monitoring, for example, monitoringanti-epileptic drugs (e.g., phenytion), chemotherapy (e.g., adriamycin),hyperactivity (e.g., ritalin), and anti-organ-rejection (e.g.,cyclosporin).

[0067] The methods for measuring the concentration of a target analytecan be generalized as follows. An initial step (Step A) entailsobtaining a raw signal from a sensing device, which signal is related toa target analyte present in the biological system. The is raw signal canbe obtained using any suitable sensing methodology including, forexample, methods which rely on direct contact of a sensing apparatuswith the biological system; methods which extract samples from thebiological system by invasive, minimally invasive, and non-invasivesampling techniques, wherein the sensing apparatus is contacted with theextracted sample; methods which rely on indirect contact of a sensingapparatus with the biological system; and the like. In preferredembodiments of the invention, methods are used to extract samples fromthe biological sample using minimally invasive or non-invasive samplingtechniques. The sensing apparatus used with any of the above-notedmethods can employ any suitable sensing element to provide the signalincluding, but not limited to, physical, chemical, electrochemical,photochemical, spectrophotometric, polarimetric, calorimetric,radiometric, or like elements. In preferred embodiments of theinvention, a biosensor is used which comprises an electrochemicalsensing element.

[0068] After the raw signal has been obtained, the signal can undergo adata screening method (Step B) in order to eliminate outlier signalsand/or poor (incorrect) signals using a predefined set of selectioncriteria. In addition, or alternatively, the raw signal can be convertedin a conversion step (Step C) which can (i) remove or correct forbackground information, (ii) integrate the signal over a sensing timeperiod, (iii) perform any process which converts the signal from onesignal type to another, or (iv) perform any combination of steps (i),(ii) and/or (iii). In preferred embodiments, the conversion step entailsa baseline background subtraction method to remove background from theraw signal and an integration step. In other embodiments, the conversionstep can be tailored for use with a sensing device that provides bothactive and reference (blank) signals; wherein mathematicaltransformations are used to individually smooth active and referencesignals, and/or to subtract a weighted reference (blank) signal from theactive signal. In still further embodiments, the conversion stepincludes correction functions which account for changing conditions inthe biological system and/or the biosensor system (e.g., temperaturefluctuations in the biological system, temperature fluctuations in thesensor element, skin conductivity fluctuations, or combinationsthereof). The result of the conversion step is an initial signal outputwhich provides a value which can be correlated with the concentration ofthe target analyte in the biological sample.

[0069] In a calibration step (Step D), the raw signal obtained from StepA, or the initial signal obtained from Step B and/or Step C, isconverted into an analyte-specific value of known units to provide aninterpretation of the signal obtained from the sensing device. Theinterpretation uses a one-to-one mathematical transformation to modelthe relationship between a measured response in the sensing device and acorresponding analyte-specific value. Thus, the calibration step is usedherein to relate, for example, an electrochemical signal (detected by abiosensor) with the concentration of a target analyte in a biologicalsystem. In one embodiment, the calibration step entails calibrating thesensing device using a single- or multi-point calibration, and thenconverting post-calibration data using correlation factors, timecorrections and constants to obtain an analyte-specific value. Furthersignal processing can be used to refine the information obtained in thecalibration step, for example, where a signal processing step is used tocorrect for signal differences due to variable conditions unique to thesensor element used to obtain the raw signal. In one embodiment, thisfurther step is used to correct for signal time-dependence, particularlysignal decline. In another embodiment, a constant offset term isobtained, which offset is added to the signal to account for a non-zerosignal at an estimated zero analyte concentration.

[0070] The analyte value obtained using the above techniques canoptionally be used in a subsequent step (Step E) to predict future (timeforecasting) or past (calibration) measurements of the target analyteconcentration in the biological system. For example, a series of analytevalues are obtained by performing any combination of Steps A, B, C,and/or D in an iterative manner. This measurement series is then used topredict unmeasured analyte values at different points in time, future orpast. In this manner, lag times inherent in certain sampling and/orsensing techniques can be reduced or eliminated to provide real timemeasurement predictions.

[0071] In another optional step, analyte values obtained using the abovetechniques can be used in a subsequent step (Step F) to control anaspect of the biological system. In one embodiment, the analyte valueobtained in Step D is used to determine when, and at what level, aconstituent should be added to the biological system in order to controlan aspect of the biological system. In a preferred embodiment, theanalyte value can be used in a feedback control loop to control aphysiological effect in the biological system.

[0072] The above general methods (Steps A through F) are eachindependently useful in analyte sensing systems and can, of course, beused in a wide variety of combinations selected for a particularbiological system, target analyte, and/or sensing technique. Forexample, in certain applications, a measurement sequence can includeSteps A, C, D, E and F, in other applications, a measurement sequencecan include Steps A, B, C and D, and the like. The determination ofparticularly suitable combinations is within the skill of the ordinarilyskilled artisan when directed by the instant disclosure. Furthermore,Steps C through F are preferably embodied as one or more mathematicalfunctions as described herein below. These functions can thus be carriedout using a microprocessor in a monitoring system. Although thesemethods are broadly applicable to measuring any chemical analyte and/orsubstance in a biological system, the invention is expressly exemplifiedfor use in a non-invasive, transdermal sampling system which uses anelectrochemical biosensor to quantify or qualify glucose or a glucosemetabolite.

[0073] Step A: Obtaining the Raw Signal.

[0074] The raw signal can be obtained using any sensing device that isoperatively contacted with the biological system. Such sensing devicescan employ physical, chemical, electrochemical, spectrophotometric,polarimetric, calorimetric, radiometric, or like measurement techniques.In addition, the sensing device can be in direct or indirect contactwith the biological system, or used with a sampling device whichextracts samples from the biological system using invasive, minimallyinvasive or non-invasive sampling techniques. In preferred embodiments,a minimally invasive or non-invasive sampling device is used to obtainsamples from the biological system, and the sensing device comprises abiosensor with an electrochemical sensing element. In particularlypreferred embodiments, a sampling device is used to obtain continualtransdermal or transmucosal samples from a biological system, and theanalyte of interest is glucose.

[0075] More specifically, a non-invasive glucose monitoring device isused to measure changes in glucose levels in an animal subject over awide range of glucose concentrations. The sampling method is based ontransdermal glucose extraction and the sensing method is based onelectrochemical detection technology. The device can be contacted withthe biological system continuously, and automatically obtains glucosesamples in order to measure glucose concentration at preprogrammedintervals.

[0076] Sampling is carried out continually by non-invasively extractingglucose through the skin of the patient. More particularly, aniontophoretic current is applied to a surface of the skin of a subject.When the current is applied, ions or charged molecules pull along otheruncharged molecules or particles such as glucose which are drawn into acollection reservoir placed on the surface of the skin. The collectionreservoir may comprise any ionically conductive material and ispreferably in the form of a hydrogel which is comprised of a hydrophilicmaterial, water and an electrolyte.

[0077] The collection reservoir may further contain an enzyme whichcatalyzes a reaction of glucose to form an easily detectable species.The enzyme is preferably glucose oxidase (GOx) which catalyzes thereaction between glucose and oxygen and results in the production ofhydrogen peroxide. The hydrogen peroxide reacts at a catalytic surfaceof a biosensor electrode, resulting in the generation of electrons whichcreate a detectable biosensor current (raw signal). Based on the amountof biosensor current created over a given period of time, a measurementis taken, which measurement is related to the amount of glucose drawninto the collection reservoir over a given period of time. In apreferred embodiment, the reaction is allowed to continue untilsubstantially all of the glucose in the collection reservoir has beensubjected to a reaction and is therefore no longer detectable, and thebiosensor current generated is related to the concentration of glucosein the subject at the approximate time of sample collection.

[0078] When the reaction is complete, the process is repeated and asubsequent measurement is obtained. More specifically, the iontophoreticcurrent is again applied, glucose is drawn through the skin surface intothe collection reservoir, and the reaction is catalyzed in order tocreate a biosensor current. These sampling (extraction) and sensingoperations are integrated such that glucose is extracted into thehydrogel collection pad where it contacts the GOx enzyme. The GOx enzymeconverts glucose and oxygen in the hydrogel to hydrogen peroxide whichdiffuses to the sensor and is catalyzed by the sensor to regenerateoxygen and form electrons. The electrons generate an electrical signalthat can be measured, analyzed, and correlated to blood glucose.

[0079] Optionally, one or more additional “active” collection reservoirs(each containing the GOx enzyme) can be used to obtain measurements. Inone embodiment, two active collection reservoirs are used, and anaverage is taken between signals from the reservoirs for eachmeasurement time point. Obtaining multiple signals, and then averagingreads from each signals, allows for signal smoothing of unusual datapoints from a sensor that otherwise may not have been detected by datascreening techniques. Furthermore, skin site variability can bedetected, and “lag” and/or “lead” differences in blood glucose changesrelative to extracted glucose changes can be mitigated. In anotherembodiment, a second collection reservoir can be provided which does notcontain the GOx enzyme. This second reservoir can serve as an internalreference (blank) for the sensing device, where a biosensor is used tomeasure the “blank” signal from the reference reservoir which signal isthen used in a blank subtraction step as described below.

[0080] A generalized method for continual monitoring of a physiologicalanalyte is disclosed in International Publication No. WO 97/24059,published Jul. 10, 1997, which publication is incorporated herein byreference. As noted in that publication, the analyte is extracted into areservoir containing a hydrogel which is preferably comprised of ahydrophilic material of the type described in International PublicationNo. WO 97/02811, published Jan. 30, 1997, which publication isincorporated herein by reference. Suitable hydrogel materials includepolyethylene oxide, polyacrylic acid, polyvinylalcohol and relatedhydrophilic polymeric materials combined with water to form an aqueousgel.

[0081] In the above non-invasive glucose monitoring device, a biosensorelectrode is positioned on a surface of the hydrogel opposite thesurface contacting the skin. The sensor electrode acts as a detectorwhich detects current generated by hydrogen peroxide in the redoxreaction, or more specifically detects current which is generated by theelectrons generated by the redox reaction catalyzed by the platinumsurface of the electrode. The details of such electrode assemblies anddevices for lontophoretic extraction of glucose are disclosed inInternational Publication No. WO 96/00110, published Jan. 4, 1996, andInternational Publication No. WO 97/10499, published Mar. 2, 1997, whichpublications are also incorporated herein by reference.

[0082] Referring now to FIGS. 1A and 1B, an iontophoretic collectionreservoir and electrode assembly for use in a transdermal sensing deviceis generally indicated at 2. The assembly comprises two iontophoreticcollection reservoirs, 4 and 6, each having a conductive medium 8, and10 (preferably cylindrical hydrogel pads), respectively disposedtherein. First (12) and second (14) ring-shaped iontophoretic electrodesare respectively contacted with conductive medium 8 and 10. The firstiontophoretic electrode 12 surrounds three biosensor electrodes whichare also contacted with the conductive medium 8, a working electrode 16,a reference electrode 18, and a counter electrode 20. A guard ring 22separates the biosensor electrodes from the iontophoretic electrode 12to minimize noise from the iontophoretic circuit. Conductive contactsprovide communication between the electrodes and an associated powersource and control means as described in detail below. A similarbiosensor electrode arrangement can be contacted with the conductivemedium 10, or the medium can not have a sensor means contactedtherewith.

[0083] Referring now to FIG. 2, an exploded view of the key componentsfrom a preferred embodiment of an iontophoretic sampling system ispresented. In FIG. 2, the iontophoretic collection reservoir andelectrode assembly 2 of FIGS. 1A and 1B is shown in exploded view incombination with a suitable iontophoretic sampling device housing 32.The housing can be a plastic case or other suitable structure whichpreferably is configured to be worn on a subjects arm in a mannersimilar to a wrist watch. As can be seen, conductive media 8 and 10(hydrogel pads) are separable from the assembly 2; however, when theassembly 2 and the housing 32 are assembled to provide an operationaliontophoretic sampling device 30, the media are in contact with theelectrodes to provide a electrical contact therewith.

[0084] In one embodiment, the electrode assemblies can include bimodalelectrodes as shown in FIG. 3.

[0085] Referring now to FIG. 5, an exploded view of the key componentsfrom one embodiment of an iontophoretic sampling system (e.g., oneembodiment of an autosensor assembly) is presented. The sampling systemcomponents include two biosensor/iontophoretic electrode assemblies, 504and 506, each of which have an annular iontophoretic electrode,respectively indicated at 508 and 510, which encircles a biosensor 512and 514. The electrode assemblies 504 and 506 are printed onto apolymeric substrate 516 which is maintained within a sensor tray 518. Acollection reservoir assembly 520 is arranged over the electrodeassemblies, wherein the collection reservoir assembly comprises twohydrogel inserts 522 and 524 retained by a gel retaining layer 526 and amask layer 528.

[0086] In one embodiment, the electrode assemblies can include bimodalelectrodes as shown in FIG. 3. Modifications and additions to theembodiment of FIG. 5 will be apparent to those skilled in the art inlight of the teachings of the present specification.

[0087] The components described herein are intended for use in aautomatic sampling device which is configured to be worn like anordinary wristwatch. As described in International Publication No. WO96/00110, published Jan. 4, 1996, the wristwatch housing (not shown)contains conductive leads which communicate with the iontophoreticelectrodes and the biosensor electrodes to control cycling and providepower to the iontophoretic electrodes, and to detect electrochemicalsignals produced at the biosensor electrode surfaces. The wristwatchhousing can further include suitable electronics (e.g., microprocessor,memory, display and other circuit components) and power sources foroperating the automatic sampling system.

[0088] Modifications and additions to the embodiment of FIG. 2 will beapparent to those skilled in the art in light of the teachings of thepresent specification.

[0089] A power source (e.g., one or more rechargeable or nonrechargeablebatteries) can be disposed within the housing 32 or within the straps 34which hold the device in contact with a skin or mucosal surface of asubject. In use, an electric potential (either direct current or a morecomplex waveform) is applied between the two iontophoretic electrodes 12and 14 such that current flows from the first iontophoretic electrode12, through the first conductive medium 8 into the skin or mucosalsurface, and then back out through the second conductive medium 10 tothe second iontophoretic electrode 14. The current flow is sufficient toextract substances including an analyte of interest through the skininto one or both of collection reservoirs 4 and 6. The electricpotential may be applied using any suitable technique, for example, theapplied current density may be in the range of about 0.01 to 0.5 mA/cm².In a preferred embodiment, the device is used for continual orcontinuous monitoring, and the polarity of iontophoretic electrodes 12and 14 is alternated at a rate of about one switch every 10 seconds toabout one switch every hour so that each electrode is alternately acathode or an anode. The housing 32 can further include an optionaltemperature sensing element (e.g., a thermistor, thermometer, orthermocouple device) which monitors the temperature at the collectionreservoirs to enable temperature correction of sensor signals asdescribed in detail below. The housing can also include an optionalconductance sensing element (e.g., an integrated pair of electrodes)which monitors conductance at the skin or mucosal surface to enable datascreening correction or invalidation of sensor signals as also describedin detail below.

[0090] After a suitable iontophoretic extraction period, one or both ofthe sensor electrode sets can be activated in order to detect extractedsubstances including the analyte of interest. Operation of theiontophoretic sampling device 30 is controlled by a controller 36 (e.g.,a microprocessor), which interfaces with the iontophoretic electrodes,the sensor electrodes, the power supply, the optional temperature and/orconductance sensing elements, a display and other electronics. Forexample, the controller 36 can include a programmable a controlledcircuit source/sink drive for driving the iontophoretic electrodes.Power and reference voltage are provided to the sensor electrodes, andsignal amplifiers can be used to process the signal from the workingelectrode or electrodes. In general, the controller discontinues theiontophoretic current drive during sensing periods. A sensor confidenceloop can be provided for continually monitoring the sampling system toinsure proper operations.

[0091] In a further aspect, the sampling device can operate in analternating polarity mode using first and second bimodal electrodes(FIG. 4, 40 and 41) and two collection reservoirs (FIG. 4, 47 and 48).Each bi-modal electrode (FIG. 3, 30; FIG. 4, 40 and 41) serves twofunctions depending on the phase of the operation: (1) anelectro-osmotic electrode (or iontophoretic electrode) used toelectrically draw analyte from a source into a collection reservoircomprising water and an electrolyte, and to the area of the electrodesubassembly; and (2) as a counter electrode to the first sensingelectrode at which the chemical compound is catalytically converted atthe face of the sensing electrode to produce an electrical signal.

[0092] The reference (FIG. 4, 44 and 45; FIG. 3, 32) and sensingelectrodes (FIG. 4, 42 and 43; FIG. 3, 31), as well as, the bimodalelectrode (FIG. 4, 40 and 41; FIG. 3, 30) are connected to a standardpotentiostat circuit during sensing. In general, practical limitationsof the system require that the bimodal electrode will not act as both acounter and iontophoretic electrode simultaneously.

[0093] The general operation of an iontophoretic sampling system is thecyclical repetition of two phases: (1) a reverse-iontophoretic phase,followed by a (2) sensing phase. During the reverse iontophoretic phase,the first bimodal electrode (FIG. 4, 40) acts as an iontophoreticcathode and the second bimodal electrode (FIG. 4, 41) acts as aniontophoretic anode to complete the circuit. Analyte is collected in thereservoirs, for example, a hydrogel (FIG. 4, 47 and 48). At the end ofthe reverse iontophoretic phase, the iontophoretic current is turnedoff. During the sensing phase, in the case of glucose, a potential isapplied between the reference electrode (FIG. 4, 44) and the sensingelectrode (FIG. 4, 42). The chemical signal reacts catalytically on thecatalytic face of the first sensing electrode (FIG. 4, 42) producing anelectrical current, while the first bi-modal electrode (FIG. 4, 40) actsas a counter electrode to complete the electrical circuit.

[0094] The electrode described is particularly adapted for use inconjunction with a hydrogel collection reservoir system for monitoringglucose levels in a subject through the reaction of collected glucosewith the enzyme glucose oxidase present in the hydrogel matrix.

[0095] The bi-modal electrode is preferably comprised of Ag/AgCl. Theelectrochemical reaction which occurs at the surface of this electrodeserves as a facile source or sink for electrical current. This propertyis especially important for the iontophoresis function of the electrode.Lacking this reaction, the iontophoresis current could cause thehydrolysis of water to occur at the iontophoresis electrodes causing pHchanges and possible gas bubble formation. The pH changes to acidic orbasic pH could cause skin irritation or burns. The ability of an Ag/AgClelectrode to easily act as a source of sink current is also an advantagefor its counter electrode function. For a three electrodeelectrochemical cell to function properly, the current generationcapacity of the counter electrode should not limit the speed of thereaction at the sensing electrode. In the case of a large sensingelectrode, the counter electrode should be able to sourceproportionately larger currents.

[0096] The design of the sampling system provides for a larger sensingelectrode (see for example, FIG. 3) than previously designed.Consequently, the size of the bimodal electrode should be sufficient sothat when acting as a counter electrode with respect to the sensingelectrode the counter electrode does not become limiting the rate ofcatalytic reaction at the sensing electrode catalytic surface.

[0097] Two methods exist to ensure that the counter electrode does notlimit the current at the sensing electrode: (1) the bi-modal electrodeis made much larger than the sensing electrode, or (2) a facile counterreaction is provided.

[0098] During the reverse iontophoretic phase, the power source providesa current flow to the first bi-modal electrode to facilitate theextraction of the chemical signal into the reservoir. During the sensingphase, the power source is used to provide voltage to the first sensingelectrode to drive the conversion of chemical signal retained inreservoir to electrical signal at the catalytic face of the sensingelectrode. The power source also maintains a fixed potential at theelectrode where, for example hydrogen peroxide is converted to molecularoxygen, hydrogen ions, and electrons, which is compared with thepotential of the reference electrode during the sensing phase. While onesensing electrode is operating in the sensing mode it is electricallyconnected to the adjacent bimodal electrode which acts as a counterelectrode at which electrons generated at the sensing electrode areconsumed.

[0099] The electrode sub-assembly can be operated by electricallyconnecting the bimodal electrodes such that each electrode is capable offunctioning as both an iontophoretic electrode and counter electrodealong with appropriate sensing electrode(s) and reference electrode(s),to create standard potentiostat circuitry.

[0100] A potentiostat is an electrical circuit used in electrochemicalmeasurements in three electrode electrochemical cells. A potential isapplied between the reference electrode and the sensing electrode. Thecurrent generated at the sensing electrode flows through circuitry tothe counter electrode (i.e., no current flows through the referenceelectrode to alter its equilibrium potential). Two independentpotentiostat circuits can be used to operate the two biosensors. For thepurpose of the present sampling system, the electrical current measuredat the sensing electrode subassembly is the current that is correlatedwith an amount of chemical signal.

[0101] With regard to continual operation for extended periods of time,Ag/AgCl electrodes are provided herein which are capable of repeatedlyforming a reversible couple which operates without unwantedelectrochemical side reactions (which could give rise to changes in pH,and liberation of hydrogen and oxygen due to water hydrolysis). TheAg/AgCl electrodes of the present sampling system are thus formulated towithstand repeated cycles of current passage in the range of about 0.01to 1.0 mA per cm² of electrode area. With regard to high electrochemicalpurity, the Ag/AgCl components are dispersed within a suitable polymerbinder to provide an electrode composition which is not susceptible toattack (e.g., plasticization) by components in the collection reservoir,e.g., the hydrogel composition. The electrode compositions are alsoformulated using analytical- or electronic-grade reagents and solvents,and the polymer binder composition is selected to be free ofelectrochemically active contaminants which could diffuse to thebiosensor to produce a background current.

[0102] Since the Ag/AgCl iontophoretic electrodes must be capable ofcontinual cycling over extended periods of time, the absolute amounts ofAg and AgCl available in the electrodes, and the overall Ag/AgClavailability ratio, can be adjusted to provide for the passage of highamounts of charge. Although not limiting in the sampling systemdescribed herein, the Ag/AgCl ratio can approach unity. In order tooperate within the preferred system which uses a biosensor having ageometric area of 0.1 to 3 cm², the iontophoretic electrodes areconfigured to provide an approximate electrode area of 0.3 to 1.0 cm²,preferably about 0.85 cm². These electrodes provide for reproducible,repeated cycles of charge passage at current densities ranging fromabout 0.01 to 1.0 mA/cm² of electrode area. More particularly,electrodes constructed according to the above formulation parameters,and having an approximate electrode area of 0.85 cm², are capable of areproducible total charge passage (in both anodic and cathodicdirections) of 270 mC, at a current of about 0.3 mA (current density of0.35 mA/cm²) for 48 cycles in a 24 hour period.

[0103] Once formulated, the Ag/AgCl electrode composition is affixed toa suitable rigid or flexible nonconductive surface as described abovewith respect to the biosensor electrode composition. A silver (Ag)underlayer is first applied to the surface in order to provide uniformconduction. The Ag/AgCl electrode composition is then applied over theAg underlayer in any suitable pattern or geometry using various thinfilm techniques, such as sputtering evaporation, vapor phase deposition,or the like, or using various thick film techniques, such as filmlaminating, electroplating, or the like. Alternatively, the Ag/AgClcomposition can be applied using screen printing, pad printing, inkjetmethods, transfer roll printing, or similar techniques. Preferably, boththe Ag underlayer and the Ag/AgCl electrode are applied using a lowtemperature screen print onto a polymeric substrate. This lowtemperature screen print can be carried out at about 125 to 160° C., andthe screening can be carried out using a suitable mesh, ranging fromabout 100-400 mesh.

[0104] User control can be carried out using push buttons located on thehousing 32, and an optional liquid crystal display (LCD) can providevisual prompts, readouts and visual alarm indications. Themicroprocessor generally uses a series of program sequences to controlthe operations of the sampling device, which program sequences can bestored in the microprocessor's read only memory (ROM). Embedded software(firmware) controls activation of measurement and display operations,calibration of analyte readings, setting and display of high and lowanalyte value alarms, display and setting of time and date functions,alarm time, and display of stored readings. Sensor signals obtained fromthe sensor electrodes are processed before storage and display by one ormore signal processing functions or algorithms which are described indetail below. The microprocessor can also include an electronicallyerasable, programmable, read only memory (EEPROM) for storingcalibration parameters (as described in detail below), user settings andall downloadable sequences.

[0105] Step B: Data Screening Methodologies.

[0106] The raw signal obtained from the above-described glucosemonitoring device can be screened to detect deviations from expectedbehavior which are indicative of poor or incorrect signals that will notcorrelate with blood glucose. Signals that are identified as poor orincorrect in this data screen may be discarded or otherwise correctedfor prior to any signal processing and/or conversion in order tomaintain data integrity. In the method of the invention, an objectiveset of selection criteria is established which can then be used toaccept or discard signals from the sensing device. These selectioncriteria are device- and analyte-specific, and can be arrived atempirically by way of testing various devices in particularapplications.

[0107] In the particular context of transdermal blood glucose monitoringusing iontophoretic extraction and electrochemical detection, thefollowing data screens can be employed. As discussed above, theiontophoretic extraction device can include two collection reservoirs.Thus, in active/blank systems, wherein one reservoir is active (containsthe GOx enzyme) and one reservoir is blank, each reservoir contains aniontophoretic electrode and a sensing electrode. Signals from both theactive and the blank reservoirs are screened, and an error in either theactive, or the active and blank signal can be used to invalidate orcorrect the measurement from the cycle. In multiple active systems(wherein two or more reservoirs contain the GOx enzyme and iontophoreticand sensing electrodes), signals from one or more of the activereservoirs are screened, and an error can be used to invalidate orcorrect the measurement from the cycle.

[0108] As with any chemical sensing method, transient changes intemperature during or between measurement cycles, or betweenmeasurements of blank and active signals, can alter background signal,reaction constants and/or diffusion coefficients. Accordingly, atemperature sensor is used to monitor changes in temperature over time.A maximum temperature change over time (d(temp)/d(time)) threshold valuecan then be used in a data screen to invalidate a measurement. Such athreshold value can, of course, be set at any objective level, which inturn can be empirically determined depending upon the particularextraction/sensing device used, how the temperature measurement isobtained, and the analyte being detected. Absolute temperature thresholdcriteria can also be employed, wherein detection of high and/or lowtemperature extremes can be used in a data screen to invalidate ameasurement. Temperature monitoring can be carried out using a separate,associated temperature sensing device, or, preferably using atemperature sensor that is integral with the sensing device. A largenumber of temperature sensing elements are known in the art (e.g.,thermometers, thermistors, thermocouples, and the like) which can beused to monitor the temperature in the collection reservoirs.

[0109] Another data screen entails monitoring physiological conditionsin the biological system, particularly monitoring for a perspirationthreshold. In this regard, perspiration contains glucose, andperspiration occurring rapidly and in sufficient quantities may affectthe detected signal either before or during biosensor measurement.Accordingly, a sensor can be used to monitor perspiration levels for agiven measurement cycle at time points before, during, and/or afteriontophoresis, and before, during, and/or after glucose sensing.Detection of perspiration levels that exceed an objective threshold isthen used in a data screen to invalidate poor measurements. Although anumber of different mechanisms can be used, skin conductance can bereadily measured with a device contacted with the skin. Skinconductivity is related to perspiration. In one embodiment, if skinconductance as measured by a conductivity detector is greater than apredetermined level, then the corresponding measurement is invalidated.

[0110] Yet further data screens which are used in the practice of theinvention take into consideration the expected behavior of thesampling/sensing device. In iontophoretic sampling, for example, thereis a skin equilibration period before which measurements will generallybe less accurate. During this equilibration period, the system voltagecan be assessed and compared against an objective high voltagethreshold. If this high voltage limit is exceeded, a data screen is usedto exclude the corresponding analyte measurement, since theiontophoretic current was not at a target value due to high skinresistance (as indicted by the high voltage level).

[0111] In addition, the electrochemical signal during each sensing cycleis expected to behave as a smooth, monotonically decreasing signal whichrepresents depletion of the hydrogen peroxide by the sensor electrode.Significant departure from this expected behavior is indicative of apoor or incorrect measurement (e.g., a non-monotonically decreasingsignal is indicative of excessive noise in the biosensor signal), andthus monitoring signal behavior during sensing operations provides yet afurther data screen for invalidating or correcting measurements.

[0112] Raw signal thresholds can also be used in the data screeningmethod of the present invention. For example, any sensor reading that isless than some minimum threshold can indicate that the sampling/sensingdevice is not operating correctly, for example, where the biosensorelectrode is disconnected. In addition, any chemical sensor will have amaximum range in which the device can operate reliably. A readinggreater than some maximal value, then, indicates that the measurement isoff-scale, and thus possibly invalid. Accordingly, minimum and maximumsignal thresholds are used herein as data screens to invalidate orcorrect measurements. Such minimum and maximum thresholds can likewisebe applied to background measurements.

[0113] A general class of screens can be applied that detect changes insignal, background, or voltage measurements. These screens are useful toassess the consistency of measurements and can detect problems orinconsistencies in the measurements. Error messages can be relayed to adisplay screen on the monitoring device, and/or, recorded to a log.Examples of such screens include the following:

[0114] (i) signal—Peak Stability. A large change in the peak of a sensorreading indicates a noisy signal. The peak of any given cathodal halfcycle is defined as the difference between the first biosensor point andthe temperature corrected average of the last two points from theprevious anodal half cycle. If the percentage difference betweensuccessive peaks from the same sensor is greater than a predeterminedvalue, for example, 30%, then an error is indicated.

[0115] (ii) background—Background Precision. Divergent readings at theend of biosensing indicate an unstable biosensor signal. Because thesereadings are used to assess background current for a particular cycle,an unstable signal may lead to an erroneous data point. If thedifference between the last two anodal points (where the last two anodalpoints are typically the last two biosensor currents measured afteranodal extraction) used to calculate the baseline is greater than orequal to a predetermined value, for example, 6 nA (or, e.g., apercentage of the first anodal point relative to the second anodalpoint), then an error is indicated.

[0116] (iii) background—Background Stability. This check is to determineif the background current is changing too excessively, which indicates anoisy signal and can result in inaccurate glucose readings. If thepercentage difference between successive background measurements isgreater than or equal to a predetermined value, for example, 15%, thenan error is indicated.

[0117] (iv) voltage—Voltage Stability. If the glucose monitoring deviceis mechanically disturbed, there can be a larger change (e.g., largerrelative to when the monitor is functioning under normal conditions) iniontophoresis voltage. This could lead to an aberrant reading. If thepercentage difference between successive cathodal or anodaliontophoresis voltages is grater than a predetermined value, forexample, 15%, then an error is indicated.

[0118] (v) voltage—Reference Electrode Check. When the electrodeassembly includes a reference electrode (as when, for example, a bimodalelectrode is employed) this check establishes the connectivity of thereference electrode to the sampling device and to the working electrode.The biosensor is activated such that a current should flow from theworking electrode to the reference electrode. If the current measured isless than a threshold value, then an error is indicated and themeasurement sequence can be terminated.

[0119] As will be appreciated by one of ordinary skill in the art uponreading this specification, a large number of other data screens can beemployed without departing from the spirit of the present invention.

[0120] Step C: The Conversion Step.

[0121] Continuing with the method of the invention, the above-describediontophoretic sampling device is used to extract the analyte from thebiological system, and a raw amperometric signal (e.g., nanoampere (nA)signal) is generated from the associated electrochemical biosensordevice. This raw signal can optionally be subjected to a data screeningstep (Step B) to eliminate poor or incorrect signals, or can be entereddirectly into a conversion step to obtain an initial signal output whichis indicative of the amount of analyte extracted by the sampling system.

[0122] I. Ways of Obtaining Integrated Signals

[0123] 1. Baseline Background.

[0124] In one embodiment, the raw or screened raw signal is processed inthe conversion step in order to remove or correct for backgroundinformation present in the signal. For example, many sensor devices willhave a signal whether or not an analyte of interest is present, i.e.,the background signal. One such background signal is the “baselinebackground,” which, in the context of electrochemical detection, is acurrent (nA) generated by the sensing device independent of the presenceor absence of the analyte of interest. This baseline backgroundinterferes with measurement of analyte of interest, and the amount ofbaseline background can vary with time, temperature and other variablefactors. In addition, electrochemically active interfering speciesand/or residual analyte can be present in the device which will furtherinterfere with measurement of the analyte of interest.

[0125] This background can be transient background, which is a currentgenerated independent of the presence or absence of the analyte ofinterest and which decreases over the time of sensor activation on thetime scale of a measurement, eventually converging with the baselinebackground signal.

[0126] Accordingly, in one embodiment of the invention, a baselinebackground subtraction method is used during the conversion step inorder to reduce or eliminate such background interferences from themeasured initial signal output. The subtraction method entailsactivation of the electrochemical sensor for a sufficient period of timeto substantially reduce or eliminate residual analyte and/orelectrochemical signal that is not due to the analyte (glucose). Afterthe device has been activated for a suitable period of time, and astable signal is obtained, a measurement is taken from the sensor whichmeasurement can then be used to establish a baseline background signalvalue. This background signal value is subtracted from an actual signalmeasurement value (which includes both analyte-specific and backgroundcomponents) to obtain a corrected measurement value. This baselinebackground subtraction method can be expressed using the followingfunction:

i(τ)=i _(raw)(τ)−i _(bkgnd)(τ)

[0127] wherein: (i_(raw)(τ)) is the current measured by the sensor (innA) at time τ; (τ) is the time after activation of the sensor;(i_(bkgnd)(τ)) is the background current (in nA); and (i(τ)) is thecorrected current (in nA). Measurement of the baseline background signalvalue is taken close in time to the actual signal measurement in orderto account for temperature fluctuations, background signal drift, andlike variables in the baseline background subtraction procedure. Thebaseline background signal value can be integrated for use withcoulometric signal processing, or used as a discrete signal value inamperometric signal processing. In particular embodiments of theinvention, continual measurement by the iontophoretic sampling deviceprovides a convenient source for the baseline background measurement,that is, after an initial measurement cycle has be completed, thebaseline background measurement can be taken from a previous measurement(sensing) cycle.

[0128] 2. Temperature Correcting Baseline Background.

[0129] In yet another embodiment of the invention, the conversion stepis used to correct for changing conditions in the biological systemand/or the biosensor system (e.g., temperature fluctuations in thebiological system, temperature fluctuations in the biosensor element, orcombinations thereof). Temperature can affect the signal in a number ofways, such as by changing background, reaction constants, and/ordiffusion coefficients. Accordingly, a number of optional temperaturecorrection functions can be used in order to reduce thesetemperature-related effects on the signal.

[0130] In order to correct for the effect that temperature fluctuationsor differences may have on the baseline background subtracted signal,the following temperature correction step can be carried out. Moreparticularly, to compensate for temperature fluctuations, temperaturemeasurements can be taken at each measurement time point within themeasurement cycle, and this information can be used to base atemperature correction algorithm which adjusts the background current atevery time point depending on the difference in temperature between thattime point and the temperature when the previous background current wasmeasured. This particular temperature correction algorithm is based onan Arrhenius relationship between the background current andtemperature.

[0131] The temperature correction algorithm assumes an Arrhenius-typetemperature dependence on the background current, such as:$i_{bkgnd} = {A\quad {\exp \left\lbrack \frac{- {K1}}{T} \right\rbrack}}$

[0132] wherein: (i_(bkgnd)) is the background current; (A) is aconstant; (K1) is termed the “Arrhenius slope” and is an indication ofhow sensitive the current is to changes in temperature; and (T) is thetemperature in ° K.

[0133] Plotting the natural log of the background current versus thereciprocal of temperature provides a linear function having a slope of(−K1). Using a known or derived value of K1 allows the baseline currentat any time (τ) to be corrected using the following function (which isreferred to herein as the “K1 temperature correction”):$i_{{bkgnd},{corrected}} = {i_{{bkgnd},\tau_{0}}{\exp \left\lbrack {- {{K1}\left( {\frac{1}{T_{\tau}} - \frac{1}{T_{\tau_{0}}}} \right)}} \right\rbrack}}$

[0134] wherein: (i_(bkgnd,corrected)) is the temperature correctedbaseline current; (i_(bkgnd,τ0)) is the baseline current at somereference temperature T_(τ0), for example, the baseline backgroundmeasurement temperature; (K1) is the temperature correction constant;and (T_(τ)) is the temperature at time τ. For the purposes of theinvention, (i_(bkgnd,τ0)) is usually defined as the “previous” baselinecurrent. As can be seen, instead of making a time-independent estimationof the baseline current, the K1 temperature correction adjusts thebaseline current in an Arrhenius fashion depending upon whether thetemperature increases or decreases during or between biosensor cycles.Determination of the constant K1 can be obtained by plotting the naturallog of the background current versus the reciprocal of the temperaturefor a learning set of data, and then using a best fit analysis to fitthis plot with a line having a slope (−K1).

[0135] Raw or screened amperometric signals from Step A or Step B,respectively (whether or not subjected to the above-described baselinebackground subtraction and/or K1 temperature correction), can optionallybe refined in the conversion step to provide integrated coulometricsignals. In one particular embodiment of the invention, any of the aboveamperometric signals (e.g., the current generated by the sensor) can beconverted to a coulometric signal (nanocoulombs (nC)), which representsthe integration of the current generated by the sensor over time toobtain the charge that was produced by the electrochemical reaction.

[0136] In one embodiment, integration is carried out by operating thebiosensor in a coulometric (charge-measuring) mode. Measuring the totalamount of charge that passes through the biosensor electrode during ameasurement period is equivalent to mathematically integrating thecurrent over time. By operating in the coulometric mode, changes indiffusion constants resulting from temperature fluctuations, possiblechanges in the diffusion path length caused by uneven or non-uniformreservoir thickness, and changes in sensor sensitivity, have littleeffect on the integrated signal, whereas these parameters may have agreater effect on single point (current) measurements. Alternatively, afunctionally equivalent coulometric measurement can be mathematicallyobtained in the method of the invention by taking discrete currentmeasurements at selected, preferably small, time intervals, and thenusing any of a number of algorithms to approximate the integral of thetime-current curve. For example, integrated signal can be obtained asfollows:

Y=∫ _(τ) ₁ ^(τ) ^(₂) i(τ)dτ

[0137] wherein: (Y) is the integrated signal (in nC); and (i(τ)) is acurrent at time τ, and can be equal to I_(raw)(τ) for an uncorrected rawsignal, or i_(raw)(τ)−i_(bkgnd)(τ) for a baseline background subtractedsignal, or i_(raw)(τ)−i_(bkgnd,corrected)(τ) for a baseline backgroundsubtracted and temperature corrected signal.

[0138] 3. Temperature Correction of Active versus Blank Integrals.

[0139] An additional temperature correction algorithm can be used hereinto compensate for temperature dependence of a transient background(blank) signal. That is, in the active/blank sampling system exemplifiedhereinabove, the analyte measurement (blood glucose) is generated byintegrating an active signal and subtracting therefrom a blank signal(see the blank subtraction method, infra). The blank integral may be“artifactually” high or low depending upon whether blank signal wasmeasured at a higher or lower temperature than the active signal. Inorder to normalize the blank integral to the temperature at which theactive signal was measured, the following function can be used (which isreferred to herein as the “K2 temperature correction”):$Y_{{blank},{corrected}} = {Y_{blank}{\exp \left\lbrack {- {{K2}\left( {\frac{1}{\overset{\_}{T_{act}^{n}}} - \frac{1}{\overset{\_}{T_{blank}^{n}}}} \right)}} \right\rbrack}}$

[0140] wherein: (Y_(blank,corrected)) is the corrected blank integral;(Y_(blank)) is the uncorrected blank integral (in nC); (K2) is the“blank integral correction constant”; and (T^(n) _(act)) and (T^(n)_(blank)) are the average temperature of the active and blank signal,respectively. The average temperature is obtained from averaging thefirst n temperatures, such that (n) is also an adjustable parameter.Determination of the constant K2 can be obtained from an Arrhenius plotof the log of the blank integral against 1/T^(n) _(blank), using thereciprocal of the average of the first n temperature values, and thenusing a best fit analysis to fit this plot with a line having a slope(−K2).

[0141] Alternative temperature corrections which can be performed duringthe conversion step are as follows. In one embodiment, an integralaverage temperature correction is used wherein, for each measurementcycle, the integral average temperature is determined by the function:$< T>={\frac{1}{T_{f}}{\int_{0}^{T_{f}}{T{t}}}}$

[0142] and then correcting for the temperature at subsequent time pointsusing the function:$Y_{t,{corrected}} = {Y_{t}{\exp \left\lbrack {- {a\left( \frac{< T_{t} > {- {< T_{ref} >}}}{< T_{ref} >} \right)}} \right\rbrack}}$

[0143] wherein: (Y_(t)) is the uncorrected signal at time t;(Y_(t,corrected)) is the corrected signal at time t; (<T_(t)>) is theintegral average temperature at time t; (<T_(ref)>) is the integralaverage temperature at the reference time (e.g., the calibration time);(t) is the time after sensor measurement is first initiated; and (a) isan adjustable parameter which is fit to the data.

[0144] In other embodiments, temperature correction functions can beused to correct for temperature differences between multiple activesignals, or between active and blank signals. For example, in theactive/blank sensing device exemplified herein, blank subtraction isused to cancel out much of the temperature-dependence in the activesignal. However, temperature transients during the monitoring periodwill result in varying background currents, which can result in signalerrors when the current is multiplied by the total integration time inthe instant conversion step. This is particularly true where the activeand blank integrals are disjointed in time, and thus possibly comprisedof sets of background current values that occurred at differenttemperatures.

[0145] 4. Anodal Subtraction.

[0146] In yet another alternative temperature correction, temperaturemeasurements taken in the active and blank reservoirs at alternatinganodal and cathodal phases during a measurement cycle are used in asubtraction method in order to reduce the impact of temperaturefluctuations on the signals. In this regard, the active/blank reservoiriontophoretic sampling system can be run under conditions whichalternate the active and blank reservoirs between anodal and cathodalphases during a measurement cycle. This allows the blank anodal signalto be measured at the same time as the active cathode signal, andtemperature variations will likely have similar impact on the twosignals. The temperature correction function thus subtracts an adjustedanodal signal (taken at the same time as the cathodal signal) from thecathodal signal in order to account for the effect of temperature on thebackground. More particularly, a number of related temperaturecorrection functions which involve fractional subtraction of blank anodesignals can be summarized as follows: $\begin{matrix}{Y = {Y_{{act},{cath}} - {d*Y_{{blank},{an}}}}} \\{Y = {Y_{{act},{cath}} - {d*\left\lbrack {Y_{{blank},{an}} - \left( {Y_{{act},{an}} - Y_{{blank},{cath}}} \right)} \right\rbrack}}} \\{Y = {Y_{{act},{cath}} - {d*\left\lbrack {Y_{{blank},{an}} - \left( {Y_{{act},{an}} - Y_{{blank},{cath}}} \right)} \right\rbrack {_{{{ave}\quad t_{1}},t_{2}}\quad}}}} \\{Y = {Y_{{act},{cath}} - {d*\left\lbrack {Y_{{blank},{an}}\left( {Y_{{blank},{an}} - Y_{{blank},{cath}}} \right)} \right\rbrack _{{ave}\quad {t_{1}--}t_{2}}}}} \\{Y = {Y_{{act},{cath}} - {d*\left( {Y_{{blank},{an}} - {AOS}} \right)*\left\lbrack \frac{Y_{{blank},{cath}}}{Y_{{act},{an}} - {AOS}} \right\rbrack _{{{ave}\quad t_{1}},t_{2}}}}} \\{Y = {Y_{{act},{cath}} - {d*\left( {Y_{{blank},{an}} - {AOS}} \right)*\left\lbrack \frac{Y_{{blank},{cath}}}{Y_{{act},{an}} - {AOS}} \right\rbrack _{{ave}\quad {t_{1}--}t_{2}}}}}\end{matrix}$

[0147] wherein: (Y_(act, cath)) is the active signal in the cathodalphase (in nC); (Y_(blank, an)) is the blank signal in the anodal phase(in nC); (Y_(act, an)) is the active signal in the anodal phase (in nC);(Y_(blank, cath)) is the blank signal in the cathodal phase (in nC); (Y)is the “blank anode subtracted” signal; (ave t₁, t₂) is the average ofsignals taken at two time points t₁ and t₂; (ave t₁--t₂) is the averageof signals taken over the time period of t₁-t₂; (d) is a universalfractional weight and is generally a function of time; and (AOS) is auniversal anodal offset which can be empirically obtained using standardmathematical techniques, and optionally adjusted using data taken fromtwo previous time points, t₁ and t₂ (i.e., ave t₁, t₂) or using theaverage of data taken over the time period of t₁-t₂ (i.e., ave t₁--t₂).

[0148] In still further embodiments of the invention, the conversionstep can include a blank subtraction step, combined data from two activereservoirs, and/or a smoothing step.

[0149] The blank subtraction step is used to subtract the blank signalfrom the active signal in order to remove signal components that are notrelated to the analyte, thus obtaining a cleaner analyte signal. Whenraw signal is obtained from two active reservoirs the two raw signalscan be averaged or a summed value of the two raw signals can be used. Inthe smoothing step, mathematical transformations are carried out whichindividually smooth signals obtained from the active and blankcollection reservoirs. These smoothing algorithms help improve thesignal-to-noise ratio in the biosensor, by allowing one to correct thesignal measurements obtained from the device to reduce unwanted noisewhile maintaining the actual signal sought.

[0150] More particularly, a blank subtraction step is used in theactive-blank iontophoretic sampling system of the invention as follows.Signals from the blank (second) reservoir, taken at, or about the sametime as signals from the active (first) reservoir, are used tosubstantially eliminate signal components from the active signal thatare not specifically related to the analyte. In this regard, the blankreservoir contains all of the same components as the active reservoirexcept for the GOx enzyme, and the blank signal should thus exhibitsimilar electrochemical current to the active signal, except for thesignal associated with the analyte. Accordingly, the following functioncan be used to subtract the blank signal from the active signal: Y _(t)=Y _(t,act) −d*Y _(t,blank)

[0151] wherein: (Y_(t,act)) is the active signal (in nC) at time t;(Y_(t,blank)) is the blank signal (in nC) at time t; (Y_(t)) is the“blank subtracted” signal at time t; and (d) is the time-dependentfractional weight for the blank signal, and d preferably=1. In relationto the equation shown above that is used to subtract the blank signalfrom the active signal, when two active reservoirs are used dpreferably=−1, or, more generally, as shown in the equation below, thesummed signal can be “weighted” to account for different contributionsof signal from each reservoir.

[0152] In the case of two active reservoirs, each reservoir is capableof generating raw signal and each contains all of the same components.For example, where two collection reservoirs are used for detectingglucose both reservoirs contain glucose oxidase. Accordingly, thefollowing function can be used:

Y _(t,ε) =aY _(t,act1) +bY _(t,act2)

[0153] wherein: “a” is the time-dependent fractional weight for thefirst active signal; (Y_(t,act1)) is the first active signal (in nC) attime t; “b” is the time-dependent fractional weight for the secondactive signal; (Y_(t,act2)) is the second active signal (in nC) at timet; (Y_(t,ε)) is the summed signal at time t.

[0154] II. General Procedures for Smoothing Integrated Signals.

[0155] In the smoothing step, the active signal obtained from the first(active) reservoir can be smoothed using a smoothing function. Inmultiple active systems, the same smoothing can be applied to eachsignal before summing. In one embodiment, the function can be expressedas a recursive function as follows:

E _(t,act) =w _(act) Y _(t,act)+(1−w _(act))(E _(t−1,act))

[0156] wherein: (Y_(t,act)) is the measurement of the active signal (innC) at time t; (E_(t,act)) is the estimate of the active signal (in nC)at time t for t>1 (at t=1, E_(t,act)=Y_(t,act)) and (w_(act)) is the“estimate weight” for the active biosensor, wherein 0≦w_(act)<1.

[0157] The reference (blank) signal obtained from the second reservoircan also be smoothed using a similar recursive smoothing function. Thisfunction can be expressed as follows: E _(t,blank) =w _(blank) Y_(t,blank)+(1−w _(blank))(E _(t−1,blank))

[0158] wherein: (Y_(t,blank)) is the measurement of the blank signal (innC) at time t; (E_(t,blank)) is the estimate of the blank signal (in nC)at time t for t>1 (at t=1, E_(t,blank)=Y_(t,blank)); and (w_(blank)) isthe “estimate weight” for the blank biosensor, wherein 0<w_(blank)<1.

[0159] Once the active and blank signals have been individuallysmoothed, the blank signal can be subtracted from the active signal inorder to obtain a signal that is indicative of the glucose reactiononly. As discussed above, the blank signal should exhibit a similarelectrochemical current to the active signal, except for the signalassociated with the glucose analyte. In the practice of the invention,this blank subtraction step can subtract the value of the smoothed blanksignal per se, or a weighted blank signal can be subtracted from theactive signal, using the following function to obtain a fractionalsubtraction:

E _(t) =E _(t,act) −d*E _(t,blank)

[0160] wherein: (E_(t,act)) is the estimate of the active signal (in nC)at time t; (E_(t,blank)) is the estimate of the blank signal (in nC) attime t; (E_(t)) is the “blank subtracted” smoothed sensor signal at timet; and (d) is the time-dependent fractional weight for the blank signal.

[0161] The same recursive function can be used wherein the order of thesmoothing and blank subtraction steps are reversed such that:(Y_(t,act)) is the integral of the active signal (in nC) at time t;(Y_(t,blank)) is the integral of the blank signal (in nC) at time t;(Y_(t)) is the “blank subtracted” sensor signal (in nC) at time t; (d)is the time-dependent fractional weight for the blank signal; and

Y _(t) =Y _(t,act) −d*Y _(t,blank)

E _(t) =wY _(t)+(1−w)(E _(t−1))

[0162] This smoothing can alternatively be carried out on discrete (nA)sensor signals, with or without temperature and/or backgroundsubtraction corrections. Smoothing can also be carried out on activesignals or on averages of two or more active signals. Furthermodifications to these functions will occur to those of ordinary skillin the art, in light of the present enabling disclosure.

[0163] Step D: The Calibration Step.

[0164] Continuing with the method of the invention, any of the rawsignals obtained from Step A, the screened raw signal obtained from StepB, or the initial output signal obtained from Step C (or from Steps Band C), can be converted into an analyte-specific value using acalibration step which correlates the signal obtained from the sensingdevice with the concentration of the analyte present in the biologicalsystem. A wide variety of calibration techniques can be used tointerpret such signals. These calibration techniques apply mathematical,statistical and/or pattern recognition techniques to the problem ofsignal processing in chemical analyses, for example, using neuralnetworks, genetic algorithm signal processing, linear regression,multiple-linear regression, partial linear regression, deconvolution, orprincipal components analysis of statistical (test) measurements.

[0165] One method of calibration involves estimation techniques. Tocalibrate an instrument using estimation techniques, it is necessary tohave a set of exemplary measurements with known concentrations referredto as the calibration set (e.g., reference set). This set consists of msamples, each with n instrument variables contained in an m by n matrix(X), and an m by 1 vector (y), containing the concentrations. If apriori information indicates the relationship between the measurementand concentration is linear, the calibration will attempt to determinean n by 1 transformation or mapping (b), such that

y=Xb

[0166] is an optimal estimate of y according to a predefined criteria.Numerous suitable estimation techniques useful in the practice of theinvention are known in the art. These techniques can be used to provideconstant parameters, which can then be used in a mathematicaltransformation to obtain a measurement value indicative of theconcentration of analyte present in the biological system at the timesof measurement.

[0167] In one particular embodiment, the calibration step may be carriedout using artificial neural networks or genetic algorithms. Thestructure of a particular neural network algorithm used in the practiceof the invention can vary widely; however, the network should contain aninput layer, one or more hidden layers, and one output layer. Suchnetworks can be optimized on training data set, and then applied to apopulation. There are an infinite number of suitable network types,transfer functions, training criteria, testing and application methods,which will occur to the ordinarily skilled artisan upon reading theinstant specification.

[0168] In the context of the iontophoretic glucose sampling devicedescribed hereinabove (which can contain an active collectionreservoir—with the GOx enzyme, and a blank collection reservoir; oralternately, two active reservoirs with the GOx enzyme), a preferredneural network algorithm would use, for example, inputs selected fromthe following to provide a blood glucose measurement: elapsed time sincecalibration; signal from the active reservoir; signal from the blankreservoir; signal from two active reservoirs (either averaged orsummed); calibration time; measured temperature; applied iontophoreticvoltage; skin conductance; blood glucose concentration, determined by anindependent means, at a defined calibration point; background;background referenced to calibration; and, when operating in thetraining mode, measured glucose.

[0169] Whether or not the calibration step is carried out usingconventional statistical techniques or neural network algorithms, thecalibration step can include a universal calibration process, asingle-point calibration process, or a multi-point calibration process.In one embodiment of the invention, a universal calibration process isused, wherein the above mathematical techniques are used to derive acorrelation factor (or correlation algorithm) that allows for accurate,dependable quantification of analyte concentration by accounting forvarying backgrounds and signal interferences irrespective of theparticular biological system being monitored. In this regard, theuniversal calibrant is selected to provide a close correlation (i.e.,quantitative association) between a particular instrument response and aparticular analyte concentration, wherein the two variables arecorrelated.

[0170] In another embodiment, a single-point calibration is used. Moreparticularly, the single-point calibration process can be used tocalibrate measurements obtained by iontophoretic sampling methodologiesusing a reference measurement obtained by conventional (invasive)methods. Single-point calibration allows one to account for variablesthat are unique to the particular biological system being monitored, andthe particular sensing device that is being used. In this regard, thetransdermal sampling device is generally contacted with the biologicalsystem (placed on the surface of a subject's skin) upon waking. Afterthe device is put in place, it is preferable to wait a period of time inorder allow the device to begin normal operations.

[0171] Further, the sampling system can be pre-programmed to beginexecution of its signal measurements (or other functions) at adesignated time. One application of this feature is to have the samplingsystem in contact with a subject and to program the sampling system tobegin sequence execution during the night so that it is available forcalibration immediately upon waking. One advantage of this feature isthat it removes any need to wait for the sampling system to warm-upbefore calibrating it.

[0172] In the context of glucose monitoring, a blood sample can beextracted when the device has attained normal operations, such that theinvasive blood sample extraction is taken in a corresponding time periodwith a measurement cycle. Actual blood glucose levels can then bedetermined using any conventional method (e.g., calorimetric,electrochemical, spectrophotometric, or the like) to analyze theextracted sample. This actual value is then used as a reference value inthe single-point calibration process, wherein the actual value iscompared against the corresponding measured value obtained with thetransdermal sampling device. In yet another embodiment, a multi-pointcalibration process is used, wherein the above-described single-pointcalibration process is repeated at least once to provide a plurality ofpoint calibrations. For example, the multi-point calibration process canbe carried out at various time intervals over the course of a continualor continuous measuring period.

[0173] Continuing with the calibration step, the signals obtained fromStep B and/or Step C, supra, can be subjected to further signalprocessing prior to calibration as follows. Referring particularly tothe baseline background subtraction method of the conversion step (StepC), the corrected signal should theoretically be directly proportionalto the amount of analyte (glucose) present in the iontophoreticallyextracted sample. However, sometimes a non-zero intercept is obtained inthe correlation between signal and reference glucose value. Accordingly,a constant offset term (which can be positive or negative) is obtainedwhich can be added to the converted signal to account for a non-zerosignal at an estimated zero blood glucose concentration. The offset canbe added to the active sensor signal; or, in the case of aniontophoretic sampling system that obtains both active and blanksignals, the offset can be added to the blank-subtracted active signal.

[0174] The calibration step can be carried out using, for example, thesingle-point calibration method described hereinabove. The referenceblood glucose concentration thus obtained can then be used in thefollowing conversion factor:$b_{gain} = \frac{{BG}_{cal} + \rho}{E_{cal} + {OS}}$

[0175] wherein: (E_(cal)) is the blank-subtracted smoothed sensor signal(in nC) at calibration; (BG_(cal)) is the reference blood glucoseconcentration (in mg/dL) at calibration; (b_(gain)) is the conversionfactor [(mg/dL)/nC]; (OS) is the offset calibration factor constant (innC) which can be calculated using standard regression analysis; and (p)is the calibration offset (in mg/dL). Post calibration data can then beconverted using the following function:

EG _(t) =b _(gain) [E _(t) +OS]−ρ

[0176] wherein (EG_(t)) is the estimated blood glucose concentration (inmg/dL). Other signal values, such as Y_(t), can be substituted for E_(t)and E_(cal) depending upon the amount of prior signal processingperformed (see, e.g., Step C, supra).

[0177] Further signal processing can also be used to correct fortime-dependent behavior related to the particular sensor element that isused in the sensing operation. In this regard, signal measurements ofcertain types (such as the electrochemical signal measurements describedherein) exhibit change over time for reasons which are not fullyunderstood. The present invention is not premised on any particulartheory with respect to why such time-dependent change occurs. Rather,the invention recognizes that time-dependent behavior can occur, andcorrects for this behavior using one or more mathematical functions.

[0178] Thus, in one embodiment, a corrected measurement can becalculated using a mathematical function which compensates fortime-dependent decline in the biosensor signal between measurementsduring the period of continual or continuous measuring of the analyteconcentration. The correction function uses one or more additive decayparameters (α_(i)) and one or more multiplicative decay parameters(ε_(i)), (both of which are empirically determined for the biosensor),and can be expressed as follows:

EG _(t) =b _(gain) [E _(t)(1+ε_(i) t)+OS]+α _(i) t−ρ

[0179] wherein:$b_{gain} = \frac{{BG}_{cal} + \rho - {\alpha_{i}t_{cal}}}{{E_{cal}\left( {{1 +} \in_{i}t_{cal}} \right)} + {OS}}$

[0180] and (t_(cal)) is the calibration point; (EG_(t)) is the estimatedblood glucose concentration at time t; (E_(t)) is the analyte signal attime t; (OS) is the constant offset term which accounts for a non-zerosignal at an estimated zero blood glucose concentration (as describedabove); (ε) is a gain term for time-dependent signal decline and canhave multiple time segments (e.g., i=1, 2, or 3); (α) is a correctionterm for a linear time-dependent signal decline in the time segments andcan have multiple time segments (e.g., i=1, 2, or 3); (t) is the elapsedtime, and (ρ) is the calibration offset (in mg/dl).

[0181] In an alternative embodiment, a corrected measurement can becalculated using a mathematical function which compensates fortime-dependent decline in the biosensor signal between measurements,during the period of continual or continuous measuring of the analyteconcentration, by correlating signal at the beginning of the measurementseries to a unit of decay. The correction function uses an additivedecay parameter (α) and a decay correction factor (γ). This equationallows a time-dependent multiplicative correction to be applied to theintegrated signal in a manner that amplifies, to a greater extent, thosesignals that have been observed to decay at a greater rate (e.g.,empirically, signals that give lower BGain tend to decay faster). Use ofthe BGAIN factor, as described herein, can insure that a reasonablecalibration factor is obtained.

[0182] In this embodiment, EG_(t), the calculated value of blood glucoseat the measurement time, is computed as follows:${EG}_{t} = {{\left( {\left\lbrack {\frac{{BG}_{cal} - {\alpha \quad t_{cal}}}{E_{cal} + {OS}} - {\gamma \quad t_{cal}}} \right\rbrack + {\gamma \quad t}} \right)*\left( {E_{t} + {OS}} \right)} + {\alpha \quad t}}$${{where}\quad {BGAIN}} = \left\lbrack {\frac{{BG}_{cal} - {\alpha \quad t_{cal}}}{E_{cal} + {OS}} - {\gamma \quad t_{cal}}} \right\rbrack$

[0183] wherein: BG_(cal) is the true blood glucose at the calibrationpoint; E_(cal) is the analyte signal at calibration; (t_(cal)) is theelapsed time of the calibration point; (EG_(t)) is the estimated bloodglucose concentration at time t; (E_(t)) is the analyte signal at timet; (OS) is the constant offset term which accounts for a non-zero signalat an estimated zero blood glucose concentration (as described above);(γ) is a time-dependent correction term for signal decline; (α) is atime-dependent correction term for signal decline; and (t) is theelapsed time.

[0184] Employing these equations a “time segmentation” can be performedas follows: $\begin{matrix}{{BGAIN}_{1} = {{\left\lbrack {\frac{{BG}_{cal} - {\alpha_{1}\quad t_{cal}}}{E_{cal} + {OS}} - {\gamma_{1}\quad t_{cal}}} \right\rbrack \quad {if}\quad t} < t_{12}}} \\{{{BGAIN}_{2} = {{\left\lbrack {\frac{{{BG}_{cal} - {\alpha_{1}\quad t_{12}} - {\alpha_{2}\left( {t_{cal} - t_{12}} \right)}}\quad}{E_{cal} + {OS}} - {\gamma_{1}\quad t_{12}} - {\gamma_{2}\left( {t_{cal} - t_{12}} \right)}} \right\rbrack \quad {if}\quad t_{12}} < t_{cal} < t_{23}}}\quad} \\{{BGAIN}_{3} = \left\lbrack {\frac{{BG}_{cal} - {\alpha_{1}\quad t_{12}} - {{\alpha 2}\left( {t_{cal} - t_{12}} \right)}\quad - {{\alpha 3}\left( {t_{cal} - t_{23}} \right)}}{E_{cal} + {OS}} - {\gamma_{1}\quad t_{12}} - {\gamma_{2}\left( {t_{cal} - t_{12}} \right)} - {\gamma_{3}\left( {t_{cal} - t_{23}} \right)}} \right\rbrack}\end{matrix}$

[0185] if t₂₃<t_(cal)

EG _(t)=(BGAIN ₁+γ₁ t)*(E _(t) +OS)+α₁ t

[0186] if t<t₁₂

EG _(t)=(BGAIN ₂+γ₁ t ₁₂+γ₂(t−t ₁₂))*(E _(t) +OS)+α₁ t ₁₂+α₂(t−t ₁₂)

[0187] if t₁₂<t<t₂₃

EG _(t)=(BGAIN ₃+γ₁ t ₁₂+γ₂(t ₂₃ −t ₁₂)+γ₃(t−t ₂₃))*(E _(t) +OS)+α₁ t₁₂+α₂(t ₂₃ −t ₁₂)+α₃(t−t ₂₃)

[0188] if t₂₃<t

[0189] wherein: EG_(t) is the calculated value of blood glucose at themeasurement time; BG_(cal) is the true blood glucose at the calibrationpoint, t is the elapsed time (hence t_(cal) is the elapsed time at thecalibration point), OS is the offset parameter, α_(i) and γ_(i) are thetime dependent correction terms to account for the declining signal withtime. To avoid a dominant time correction term as the elapsed timeincreases, the time correction parameters α_(i) and γ_(i) are distinctfor three different time intervals (“i”): 0 to 6 hours (e.g., i=1), 6 to10 hours (e.g., i=2), and 10 to 14 hours (e.g., i=3), as shown above.Therefore, t₁₂=6 hours and t₂₃=10 hours.

[0190] The time segmentation allows for greater flexibility inpredicting non-linear signal decay terms.

[0191] The signal processing methods and techniques described in Steps Athrough D can be combined in a variety of ways to provide for improvedsignal processing during analyte measurement. In one embodiment, anactive/blank sampling system is used to obtain the raw signal in Step A.These raw signals are then screened in Step B to obtain screened data.These screened data are then subjected to a temperature correction usingthe K1 temperature correction, and then converted using the baselinesubtraction and integration methods of Step C. The converted data arealso smoothed (both active and blank) using the smoothing functions ofStep C, the smoothed data are temperature corrected using the K2temperature correction, and a blank subtraction is carried out. Thesmoothed and corrected data are then converted to the analyteconcentration in the biological system using the calibration methods ofStep D to perform a single-point calibration, wherein the data is alsorefined using the offset and time-dependent behavior corrections toobtain a highly accurate analyte concentration value.

[0192] In another embodiment, if two active reservoirs (A₁/A₂) are used,a “sensor consistency check” can be employed that detects whether thesignals from the reservoirs are changing in concert with one another.This check compares the percentage change from the calibration signalfor each reservoir, then calculates the difference in percentage changein signal between the two reservoirs. If this difference is greater thansome threshold, then the signals are not “tracking” one another and thisdata point can be screened as in Step B. This check verifies consistencybetween the two sensors. A large difference can indicate noise in thesignals.

[0193] In yet another embodiment of the present invention a “CalibrationFactor Check” may be employed. This check provides control overunreasonable finger prick measurements or incorrect entries and providesadditional assurance that a reasonable calibration slope has beengenerated. Typically, there are two calibration factors that arecalculated at calibration: BGAIN and CAL RATIO. If BGAIN is less than orequal to a predetermined threshold value, or if the CAL RATIO is greaterthan or equal to a predetermined threshold value, then a calibrationerror is indicated. Such an error can be displayed to the user, forexample, a calibration window can appear on the monitor's displayappear. Such an error indicates to the users that the user must performthe calibration again. For the Calibration Factor Check, CAL RATIO canbe calculated as follows:${CALRATIO} = \left\lbrack \frac{{BG}_{cal}}{E_{cal} + {OS}} \right\rbrack$

[0194] wherein, BG_(cal) is the true blood glucose at the calibrationpoint; E_(cal) is the analyte signal at calibration; and (OS) is theconstant offset term which accounts for a non-zero signal at anestimated zero blood glucose concentration.

[0195] Step E: Time Forecasting Measurements.

[0196] The corrected analyte value obtained using the above techniquescan be used to predict future (e.g., time forecasting) or past (e.g.,calibration) target analyte concentrations in the biological system. Inone embodiment, a series of analyte values are obtained by performingany combination of Steps A, B, C, and/or D, supra, in an iterativemanner. These measurements are then used to predict unmeasured analytevalues at different points in time, future or past.

[0197] More particularly, the above-described iontophoretic samplingprocess is carried out in order to obtain three or more measurements ofthe target analyte. Using these measurements, an additional measurementcan be calculated. The additional measurement is preferably calculatedusing a series function.

[0198] In the context of blood glucose monitoring, it has been foundthat the actual (real-time) glucose level in a subject differs from themeasured glucose level obtained using a sampling device that extractsglucose from the subject using iontophoresis. The difference is due, inpart, to a lag time between extracting the glucose analyte and obtaininga measurement from the extracted glucose. This lag time can varydepending on factors such as the particular subject using the device,the particular area of skin from which glucose is extracted, the type ofcollection reservoir used, and the amount of current applied. In orderto compensate for this inherent lag time, the method of the presentinvention can utilize data obtained from previous measurements and amathematical function in order to predict what a future analyteconcentration will be. In this case, the predicted future reading can beused as a “real-time value” of the analyte level.

[0199] In another embodiment, mathematical methods can be used topredict past measurements, such as in the context of making acalibration. More particularly, measurements obtained using theabove-described transdermal sampling device can be calibrated againstone or more reference measurements obtained by conventional (bloodextraction) methods. In such calibration processes, actual blood glucoselevels are determined using conventional analytical methods (e.g.,calorimetric, electrochemical, spectrophotometric, or the like) toanalyze an extracted blood sample. These actual measurements are thencompared with corresponding measurements obtained with the transdermalsampling device, and a conversion factor is then determined. In normaloperations, the transdermal sampling device is generally first contactedwith the biological system (placed on the surface of a subject's skin)upon waking. After the device is put in place, it is preferable to waita period of time in order allow the device to attain normal operatingparameters, after which time the device can be calibrated. However, if ablood sample is extracted at the time when the device is first applied(as would normally be most convenient), there may not be a correspondingsignal from the transdermal sampling system which can be compared withthe reference value obtained from the extracted blood sample. Thisproblem can be overcome using prediction methods which allow one toperform a conventional blood glucose test (via a blood sampleextraction) when the device is first applied, and then calibrate thedevice at a later time against the results of the conventional glucosetest.

[0200] A number of mathematical methods for predicting future or pastmeasurements can be used in the practice of the invention. For example,linear or nonlinear regression analyses, time series analyses, or neuralnetworks can be used to predict such measurements. However, it ispreferred that a novel combination of exponential smoothing and a Taylorseries analysis be used herein to predict the future or pastmeasurement.

[0201] A number of other physiological variables may be predicted usingthe above techniques. For example, these prediction methods can be usedto time forecast those physiological variables that cannot be measuredin real-time, or that demonstrate frequent fluctuations in their data.Examples of physiological functions and the variables that characterizethem include, but are not limited to, cerebral blood flow (in thetreatment of stroke patients) which is related to blood viscosity andthe concentrations of plasma proteins and clotting factors in the bloodstream (Hachinski, V. and Norris, J. W., “The Acute Stroke,”Philadelphia, F A Davis, 1985); pulmonary function (in asthma patients)as measured by lung volumes in the different phases of respiration(Thurlbeck, W. M. (1990) Clin. Chest Med. 11:389); and heart activity(in recurrent cardiac arrest) as measured by electrical activity of theheart (Marriott, H J L, “Practical Electrocardiography”, 8th Ed.,Baltimore, Williams & Wilkins, 1983). Other examples of physiologicalvariables that can be predicted, include renal dialysis, where bloodconcentrations of urea and blood gases are followed (Warnock, D. G.(1988) Kidney Int. 34:278); and anesthesia treatment, where variousparameters (e.g., heart rate, blood pressure, blood concentration of theanesthesia) are monitored to determine when the anesthesia will stopfunctioning (Vender, J. S., and Gilbert, H. C., “Monitoring theAnesthetized Patient,” in Clinical Anesthesia, 3rd Ed., by Barash etal., Lippincott-Raven Publishers, Philadelphia, 1996).

[0202] Step F: Controlling a Physiological Effect.

[0203] The analyte value obtained using the above techniques can also beused to control an aspect of the biological system, e.g., aphysiological effect. In one embodiment, an analyte value obtained asdescribed above is used to determine when, and at what level, aconstituent should be added to the biological system in order to controlthe concentration of the target analyte.

[0204] More particularly, in the context of blood glucose monitoring,use of prediction techniques (Step E, supra) allows for accuratepredictions of either real-time or future blood glucose values. This isof particular value in predicting hypoglycemic episodes which can leadto diabetic shock, or even coma. Having a series of measurementsobtained from the continual iontophoretic sampling device, and thecapability to predict future values, allows a subject to detect bloodglucose swings or trends indicative of hypoglycemic or hyperglycemicepisodes prior to their reaching a critical level, and to compensatetherefor by way of exercise, diet or insulin administration.

[0205] A feedback control application of the present invention entailsusing a function to predict real-time blood glucose levels, ormeasurement values of blood glucose levels at a different time, and thenthe same to control a pump for insulin delivery to treat hyperglycemia.

EXAMPLES

[0206] The following examples are put forth so as to provide those ofordinary skill in the art with a complete disclosure and description ofhow to make and use the devices, methods, and formulae of the presentinvention, and are not intended to limit the scope of what the inventorsregard as their invention. Efforts have been made to ensure accuracywith respect to numbers used (e.g., amounts, temperature, etc.) but someexperimental errors and deviations should be accounted for. Unlessindicated otherwise, parts are parts by weight, molecular weight isweight average molecular weight, temperature is in degrees Centigrade,and pressure is at or near atmospheric.

Example 1 Signal Processing for Measurement of Blood Glucose

[0207] In order to assess the signal processing methods of the presentinvention, an iontophoretic sampling device was used to extract a seriesof 525 blood glucose samples from an experimental population of humansubjects, and non-processed measurement values were compared againstmeasurement values obtained using the data screening and correctionalgorithm of the present invention.

[0208] More particularly, iontophoretic sampling was performed onsubjects using a GlucoWatch™ (Cygnus, Inc., Redwood City, Calif.)iontophoretic sampling system. This transdermal sampling device, whichis designed to be worn like a wrist watch, uses iontophoresis(electroosmosis) to extract glucose analyte into a collection pad wornbeneath the watch. Glucose collected into the GlucoWatch™ samplingsystem triggers an electrochemical reaction with a reagent in the pad,giving rise to a current which is sensed, measured, and converted to ablood glucose concentration. Measurements are taken on a continualbasis, wherein combined extraction and sensing (measurement cycles) wereset at 30 minutes. Iontophoresis was carried out using two collectionpads contacted with Ag/AgCl iontophoretic electrodes, an iontophoreticcurrent density of 0.3 mA/cm², and the electrical polarity of theelectrodes was switched halfway through the 30 minute measurement cycle.Sensing was carried out using platinum-based biosensor electrodes whichwere contacted with the collection pads. A description of theGlucoWatch™ sampling system can be found in publication to Conn, T. E.(Jan. 15, 1997) “Evaluation of a Non-Invasive Glucose Monitoring Systemfor People with Diabetes,” given at the Institute of Electrical andElectronics Engineers (IEEE) meeting entitled “Engineering in Medicine &Biology,” Stanford, Calif., which publication is incorporated herein byreference.

[0209] Concurrent with obtaining the calculated blood glucose values(from the GlucoWatch™ sampling system), blood samples (finger sticks)were obtained and analyzed for use as reference measurements. As aresult, 525 sets of paired measurements (reference and calculatedmeasurements) were obtained. A comparison was then made between thereference measurements and the calculated measurements (either raw, orsignal processed using the methods of the invention). Two different setsof data screens were used as follows: (a) maximum temperature changeover time (d(temp)/d(time)), perspiration threshold, and a thresholddeparture from monotonicity (this set of temperature screens isindicated as (+) in Table 1 below); or (b) maximum temperature changeover time (d(temp)/d(time)), perspiration threshold, a thresholddeparture from monotonicity, and a threshold baseline background changeover time (this set of temperature screens is indicated as (++) in Table1 below). The correction algorithm that was used is as follows:

EG _(t) =b _(gain) [E _(t)(1+ε_(i) t)+OS]+α _(i) t−ρ

[0210] wherein:$b_{gain} = \frac{{BG}_{cal} + \rho - {\alpha_{cal}t}}{{E_{cal}\left( {1 + {\varepsilon_{i}t_{cal}}} \right)} + {OS}}$

[0211] and (t_(cal)) is the calibration point; (EG_(t)) is the estimatedblood glucose concentration at time t; (E_(t)) is the analyte signal attime t; (OS) is the constant offset term which accounts for a non-zerosignal at an estimated zero blood glucose concentration (as describedabove); (ε) is a gain term for time-dependent signal decline and canhave multiple time segments (e.g., i=1, 2, or 3); (α) is a correctionterm for a linear time-dependent signal decline in the time segments andcan have multiple time segments (e.g., i=1, 2, or 3); (t) is the elapsedtime, and (ρ) is the calibration offset (in mg/dl).

[0212] In the comparison, an Error Grid Analysis (Clarke et al. (1987)Diabetes Care 10:622-628) was used to assess device effectiveness,wherein calculated measurements were plotted against the correspondingreference measurements. An effective blood glucose monitoring deviceshould have greater than approximately 85-90% of the data in the A and Bregions of the Error Grid Analysis, with a majority of the data in the Aregion (Clark et al., supra). The results of the Error Grid Analysis arepresented below in Table 1 as (A+B %). As can be seen, the combinationof data screening methods and the correction algorithm of the presentinvention met this effective criteria.

[0213] Another measure of device accuracy is the mean absolute % error(MPE(%)) which is determined from the mean of individual % error (PE)given by the following function:${PE} = \frac{{EG}_{t} - {BG}_{t}}{{BG}_{t}}$

[0214] wherein BG_(t) is the reference glucose measurement and EG_(t) isthe calculated glucose measurement. Effective measurements should have aMPE(%) of about 25% or less. The results of the MPE(%) are also depictedin Table 1. As can be seen, the combination of data screening methodsand the correction algorithm of the present invention met this effectivecriteria.

[0215] The correlation between calculated and measured blood glucosevalues was also assessed. The correlation coefficient values (R) arealso presented in Table 1 below. Effective measurements should have Rvalues of greater than about 0.85. As can be seen, the combination ofdata screening methods and the correction algorithm of the presentinvention provide for increased correlation between actual and measuredvalues. TABLE 1 525 Total Paired Data Algorithm Screen No. pts. MPE(%)A + B(%) Other(%) R 0 0 525 54 73 27 0.54 + + 467 24 90 10 0.87 + ++ 30820 91  9 0.90

What is claimed is:
 1. A method for continually measuring an analytepresent in a biological system, said method comprising: (a)transdermally extracting the analyte from the biological system using asampling system that is in operative contact with a skin or mucosalsurface of said biological system; (b) obtaining a raw signal from theextracted analyte, wherein said raw signal is related to analyteconcentration; (c) subjecting the raw signal obtained in step (b) to aconversion step in order to convert said raw signal to an initial signaloutput which is indicative of the amount of analyte extracted by thesampling system; (d) performing a calibration step which converts theinitial signal output obtained in step (c) to a measurement valueindicative of the concentration of analyte present in the biologicalsystem at the time of extraction; and (e) repeating steps (a)-(c) atleast once to obtain a plurality of measurement values, wherein thesampling system is maintained in operative contact with the skin ormucosal surface of said biological system to provide for a continualanalyte measurement.
 2. The method of claim 1, wherein the analyte isextracted from the biological system into a first collection reservoirto obtain a concentration of the analyte in said reservoir.
 3. Themethod of claim 2, wherein the first collection reservoir is in contactwith the skin or mucosal surface of the biological system and theanalyte is extracted using an iontophoretic current applied to said skinor mucosal surface.
 4. The method of claim 2, wherein the firstcollection reservoir contains an enzyme that reacts with the extractedanalyte to produce an electrochemically detectable signal.
 5. The methodof claim 4, wherein the analyte is glucose and the enzyme is glucoseoxidase.
 6. The method of claim 1, wherein the raw signal obtained instep (b) is subjected to a data screen which invalidates or correctspoor or incorrect signals based on a detected parameter indicative of apoor or incorrect signal.
 7. The method of claim 6, wherein the datascreen applies a set of selection criteria to the raw signal, whereineach selection criterium is based on a different detected parameterindicative of a poor or incorrect signal.
 8. The method of claim 6,wherein the data screen entails monitoring changes in temperature overtime during steps (a) and (b), and a maximum temperature change overtime (d(temp)/d(time)) value is used to invalidate or correctmeasurements taken during a measurement period during which the maximumd(temp)/d(time) value was exceeded.
 9. The method of claim 6, whereinthe data screen entails monitoring perspiration levels in the biologicalsystem at selected time points, and a maximum perspiration levelthreshold is used to invalidate or correct measurements taken during ameasurement period during which the maximum perspiration level thresholdwas exceeded.
 10. The method of claim 3, wherein the raw data obtainedin step (b) is subjected to a data screen which entails monitoringiontophoretic voltage during steps (a) and (b), and uses a maximumiontophoretic voltage value to invalidate or correct measurements takenduring a measurement period during which said maximum voltage value wasexceeded.
 11. The method of claim 1, wherein the conversion step entailsa baseline background subtraction method to remove background noise fromthe raw signal.
 12. The method of claim 11, wherein the baselinebackground subtraction method uses a temperature-corrected baselinevalue.
 13. The method of claim 11, wherein the baseline backgroundsubtraction method uses a skin conductivity-corrected baseline value.14. The method of claim 2, wherein the sampling system further comprisesa second collection reservoir which does not contain the enzyme, andstep (b) further entails obtaining a blank signal from said secondcollection reservoir, which blank signal is used in step (c) as a blankcorrection value to remove background information from the initialsignal output.
 15. The method of claim 2, wherein the sampling systemfurther comprises a second collection reservoir containing an enzymethat reacts with the extracted analyte to produce an electrochemicallydetectable signal, and step (b) further entails obtaining signals fromsaid first and second collection reservoirs.
 16. The method of claim 1,wherein the conversion step integrates the initial signal output over asensing time period.
 17. The method of claim 14, wherein the conversionstep uses a mathematical transformation to individually smooth thesignals obtained from the first and second collection reservoirs. 18.The method of claim 1S, wherein the conversion step uses a mathematicaltransformation to individually smooth the signals obtained from thefirst and second collection reservoirs.
 19. The method of claim 17,wherein the difference between signals obtained from the first andsecond collection reservoirs are smoothed.
 20. The method of claim 1,wherein the calibration step entails a single-point calibration againsta calibration reference value.
 21. The method of claim 1, wherein thecalibration step entails the use of a neural network algorithm thatcorrelates the initial signal output obtained in step (c) with ameasurement value indicative of the concentration of analyte present inthe biological system at the time of extraction.
 22. The method of claim1, wherein the sampling system is programmed to begin obtaining rawsignal at a designated time.
 23. The method of claim 22, wherein thedesignated time precedes step (d).
 24. The method of claim 1, whereinthe calibration step entails the use of a linear correlation tocorrelate the initial signal output obtained in step (c) with ameasurement value indicative of the concentration of analyte present inthe biological system at the time of extraction.
 25. The method of claim1, wherein the calibration step further entails compensating fortime-dependent behavior between signal measurements obtained in step(b).
 26. The method of claim 25, wherein the time-dependent behaviorcomprises signal decline between said measurements.
 27. The method ofclaim 25, wherein the compensating is carried out using the followingfunction: EG _(t) =b _(gain) [E _(t)(1+ε_(i) t)+OS]+α _(i) t−ρ wherein:$b_{gain} = \frac{{BG}_{cal} + \rho - {\alpha_{cal}t}}{{E_{cal}\left( {1 + {\varepsilon_{i}t_{cal}}} \right)} + {OS}}$

and (t_(cal)) is the calibration point; (EG_(t)) is the estimated bloodglucose concentration at time t; (E_(t)) is the analyte signal at timet; (OS) is the constant offset term which accounts for a non-zero signalat an estimated zero blood glucose concentration; (ε) is a gain term fortime-dependent signal decline and can have multiple time segments; (i)is a time segment; (α) is a correction term for a linear time-dependentsignal decline in the time segments and can have multiple time segments;(t) is the elapsed time, and (ρ) is the calibration offset.
 28. Themethod of claim 25, wherein the compensating is carried out using thefollowing function:${EG}_{t} = {{\left( {\left\lbrack {\frac{{BG}_{cal} - {\alpha \quad t_{cal}}}{E_{cal} + {OS}} - {\gamma \quad t_{cal}}} \right\rbrack - {\gamma \quad t}} \right)*\left( {E_{t} + {OS}} \right)} + {\alpha \quad t}}$${{where}\quad {BGAIN}} = \left\lbrack {\frac{{BG}_{cal} - {\alpha \quad t_{cal}}}{E_{cal} + {OS}} - {\gamma \quad t_{cal}}} \right\rbrack$

wherein: BG_(cal) is the true blood glucose at the calibration point;E_(cal) is the analyte signal at calibration; (t_(cal)) is the elapsedtime of the calibration point; (EG_(t)) is the estimated blood glucoseconcentration at time t; (E_(t)) is the analyte signal at time t; (OS)is the constant offset term which accounts for a non-zero signal at anestimated zero blood glucose concentration; (γ) is a time-dependentcorrection term for signal decline; (α) is a time-dependent correctionterm for signal decline; and (t) is the elapsed time.
 29. The method ofclaim 28, wherein a time segmentation is performed as follows:$\begin{matrix}{{BGAIN}_{1} = {{\left\lbrack {\frac{{BG}_{cal} - {{\alpha \quad}_{1}t_{cal}}}{E_{cal} + {OS}} - {\gamma_{1}\quad t_{cal}}} \right\rbrack \quad {if}\quad t} < t_{12}}} \\{{{BGAIN}_{2} = {{\left\lbrack {\frac{{BG}_{cal} - {{\alpha \quad}_{1}t_{12}} - {\alpha_{2}\left( {t_{cal} - t_{12}} \right)}}{E_{cal} + {OS}} - {\gamma_{1}t_{12}} - {\gamma_{2}\left( \quad {t_{cal} - t_{12}} \right)}} \right\rbrack \quad {if}\quad t_{12}} < \quad t_{cal} < t_{23}}}\quad} \\{{{BGAIN}_{3} = \left\lbrack {\frac{{BG}_{cal} - {{\alpha \quad}_{1}t_{12}} - {{\alpha 2}\left( {t_{cal} - t_{12}} \right)} - {{\alpha 3}\left( {t_{cal} - t_{23}} \right)}}{E_{cal} + {OS}} - {\gamma_{1}t_{12}} - {\gamma_{2}\left( \quad {t_{cal} - t_{12}} \right)} - {\gamma_{3}\left( {t_{cal} - t_{23}} \right)}} \right\rbrack}\quad} \\{{{{if}\quad t_{23}} < \quad t_{cal}}}\end{matrix}$

if t₂₃<t_(cal) EG _(t)=(BGAIN ₁+γ₁ t)*(E _(t) +OS)+α₁ t if t<t₁₂ EG_(t)=(BGAIN ₂+γ₁ t ₁₂+γ₂(t−t ₁₂))*(E _(t) +OS)+α₁ t ₁₂+α₂(t−t ₁₂) ift₁₂<t<t₂₃ EG _(t)=(BGAIN ₃+γ₁ t ₁₂+γ₂(t ₂₃ −t ₁₂)+γ₃(t−t ₂₃))*(E _(t)+OS)+α₁ t ₁₂+α₂(t ₂₃ −t ₁₂)+α₃(t−t ₂₃) if t₂₃<t wherein: EG_(t) is thecalculated value of blood glucose at the measurement time; BG_(cal) isthe true blood glucose at the calibration point, t is the elapsed time;t_(cal) is the elapsed time at the calibration point; OS is the offsetparameter; and α_(i) and γ_(i) are time dependent correction terms toaccount for declining signal with time, where i=1, 2, or
 3. 30. Themethod of claim 1, wherein the conversion step further entails using atemperature correction function to correct for changes in the biologicalsystem and/or changes in the sensing device.
 31. The method of claim 30,wherein the changes in the biological system comprise a change intemperature.
 32. The method of claim 30, wherein the conversion stepentails correcting for temperature changes occurring between ameasurement of background signal in the sensing device and measurementof a raw signal in step (b), and during the measurement of the rawsignal.
 33. The method of claim 32, wherein the temperature correctionuses an Arrhenius correction function.
 34. The method of claim 32,wherein the temperature correction uses an integral average temperaturecorrection function obtained from a measurement cycle to correct fortemperature at subsequent time points.
 35. The method of claim 30,wherein the conversion step entails correcting for temperaturedifferences between multiple signals obtained from the sensing device.36. The method of claim 1, wherein said biological system includes skin,and said extracting of analyte from the biological system into areservoir further comprises enhancement of skin permeability by prickingthe skin with micro-needles.
 37. A monitoring system for continually orcontinuously measuring an analyte present in a biological system, saidsystem comprising, in operative combination: (a) sampling means forcontinually or continuously extracting the analyte from the biologicalsystem, wherein said sampling means is adapted for extracting theanalyte across a skin or mucosal surface of said biological system; (b)sensing means in operative contact with the analyte extracted by thesampling means, wherein said sensing means obtains a raw signal from theextracted analyte and said raw signal is specifically related to theanalyte; and (c) microprocessor means in operative communication withthe sensing means, wherein said microprocessor means (i) subjects theraw signal to a conversion step to convert said raw signal to an initialsignal output which is indicative of the amount of analyte extracted bythe sampling means, and (ii) performs a calibration step whichcorrelates said initial signal output with a measurement valueindicative of the concentration of analyte present in the biologicalsystem at the time of extraction.
 38. The monitoring system of claim 37,wherein the sampling means includes one or more collection reservoirsfor containing the extracted analyte.
 39. The monitoring system of claim37, wherein the sampling means uses an iontophoretic current to extractthe analyte from the biological system.
 40. The monitoring system ofclaim 39, wherein the collection reservoir contains an enzyme thatreacts with the extracted analyte to produce an electrochemicallydetectable signal.
 41. The monitoring system of claim 40, wherein theanalyte is glucose and the enzyme is glucose oxidase.
 42. The monitoringsystem of claim 37 further comprising temperature sensing means and skinconductance sensing means for monitoring temperature and skinconductance in the monitoring system or biological system.
 43. Themonitoring system of claim 37, wherein the microprocessor is programedto begin execution of sampling and sensing at a defined time.
 44. Use ofthe monitoring system of claim 37 to continually or continuously measurean analyte present in a biological system.